DoD 2012.3 SBIR Solicitation

OBJECTIVE: Enable decision support for the supervisory control of highly autonomous systems by developing one or more Behavioral Anomaly Detection Services. The underlying algorithms, embodied as re-useable services would enable human supervisors to exploit, benefit from, and interact with technologies on the basis of their behavior, without requiring a deep understanding of the functions in the underlying systems. DESCRIPTION: To deal with the increasingly complex, dynamic and unpredictable operational environments of the 21st century, unmanned and autonomous system, sensor, and vehicle technologies are being expanded and improved. For example, unmanned air, ground, and maritime systems provide an expanding set of capabilities, such as intelligence, detection, security, targeting, and strike, while reducing the risk to human life. The goal with the employment of these systems is to shift from today"s manpower-intensive model of unmanned system control to a future model with fewer users who are supervising autonomous systems (1). However, to achieve this goal, a significant issue that needs to be addressed is determining how users can, and should, supervise these multiple autonomous systems in future environments that are unpredictable, complex, and highly dynamic. A key technology that can help users supervise these autonomous systems is the development and maturation of machine-based anomaly detection, to detect and characterize significant anomalous behaviors that might emerge within an on-going mission and task context. This technology can help users supervise systems by drawing users"limited attention to just the most critical, anomalous events and will be a key enabler to reducing the manpower required to manage autonomous systems by monitoring for anomalous events currently performed by humans. The focus of this effort is to make the anomaly detection technology relevant and useful to the future human supervisor. The output of this effort should define, structure, and enable efficient information transactions between users and the anomaly detection technology. Research will be needed to inform the development of system behavior anomaly detection algorithms, including models of system normalcy, deviations from normalcy, and mission context. A central challenge in this domain will be determining what behavior constitutes significant deviations from normal behavior. Deviations in the face of dynamic missions, and operational contexts is difficult to define, and must be relevant to the human user supervising the system. The model must be tailored to needs of user tasks and decisions, and tuned to optimize trust in automation (2, 3, and 4) and avoid the documented pitfalls of automation (5, 6). A user interface layer and an associated business process will be needed to structure and enable interactions between users and anomaly detection algorithms. The proposed research will develop techniques to enable the detection of anomalies in the behavior of command and control systems. The desired solution should be applicable to anomaly detection in a variety of command and control domains, such as multi-echelon military command and control, and the management of multiple autonomous vehicles and systems. For example, for the management of multi-UAV systems, the algorithms will detect anomalies to either make corrections within the UAVs"mission scope or alert the operator and provide alternative courses of action. Resultant capabilities are expected to produce cost savings through a reduction in manpower, as Autonomous Warfare evolves from multiple operator vehicles with teams of human controllers, to a single operator managing multiple systems. PHASE I: Survey and select available anomaly detection algorithms and technologies. Define and analyze the requirements for determining system normalcy, deviations from normalcy, and mission context for a specific autonomous system. Identify relevant literature from cognitive decision making and human supervisory control of automation to develop requirements for decision support needed for human supervision of anomaly detection technology. Design a concept prototype tool with wireframe design elements to demonstrate interactions between human supervisor and anomaly detection technology. Define operational and technical metrics that will permit the demonstration of the utility of the approach in Phase II. PHASE II: Develop, demonstrate, and refine the Phase I concept prototype. Validate utility in human performance evaluations. Demonstrate applicability to unmanned systems domain and other command and control domains. The effectiveness shall be demonstrated by satisfying the metrics defined in Phase 1, as well as additional metrics that may be developed in Phase 2. Develop a plan for transition and commercialization. PHASE III DUAL USE COMMERCIALIZATION: Refine the prototype and make its feature set complete in preparation for transition and commercialization. In addition to the DoD, there will be an increasing demand for supervision of autonomous systems in the commercial sector, such as the process control domain and commercial mining industries, and in federal and state agencies such as law enforcement, emergency management, and border protection. These domains could benefit significantly from the application of the solution developed in this effort.

OBJECTIVE: Develop a systems engineering tool to automate storage of data for autonomous systems and to deploy components necessary to implement the system. DESCRIPTION: Large amounts of data are generated on autonomous systems including but not limited to imagery data, geospatial information, platform health data, and specific mission-related sensor data. Current software used to classify and utilize these data sets is largely uncoordinated across the multiple data inputs. Human operators must fill the technical gap via in-depth cross-data analysis, taking context into account. Current autonomous systems have limited ability to self-govern their data storage, perform context-driven data analysis, and share data with other autonomous platforms and/or human operators. New and/or improved software tools, computational methods, and laboratory technologies are necessary to bridge these gaps and enhance ongoing efforts. Software capable of integrating a wide range of disparate data sources and computational or mathematical methods and tools for connecting or merging models and creating bridges between models of different scale are needed for a better understanding of the processes underlying autonomous operation in a highly uncertain environment, as might be generated in a disaster event. In addition, technologies are needed to generate supporting data that enable these models to accurately represent the processes they model. Understanding and quantifying multi-component, interactive processes at the component level can be limiting. Quantitative methods and technologies to measure component and multi-component collective behavior in simulated virtual and physical environments are needed. This initiative will support the development of these enabling software packages, modeling methods, and technologies. PHASE I: The first phase consists of investigating and identifying model elements and their relationships (meta-models), as well as prototyping a graphical language to manipulate the models within the Autonomous System Demonstration and Experimentation Workbench. Model elements at the highest level include, Autonomous Key Functions, Mission Task Analysis and Test and Experiment Strategy where key functions support mission task analysis, which drive the Test and Evaluation strategy. Next, the system should support assembling collections of key functions (feature modeling) that support requirements based on mission task analysis. The collections should be combinable by an analyst, using a graphical domain specific language, to determine operationally suitable experimental system designs, cost reports, collections of specifications, simulation decks and eventually an experimental system. The system should also be able to catalog and archive these analytical exercises. The Phase I deliverables should include a final Phase I report that will include the algorithms and hardware needed to implement the workbench, as well as requirements for the Graphical Interface. Feasibility of the proposed approach should be demonstrated through simulation or implementation. PHASE II: Phase II shall produce and deliver a prototype Autonomous System Demonstration and Experimentation Workbench. The Phase II system shall be demonstrated using meta-models and data structures defined in Phase I. The prototype should include analysis examples including: Creating experimental systems for a set mission task analysis efforts, such as Urban Driving, gas monitoring in an urban environment or forest fire fighting. Creating a list of acceptable sensors for a particular task, i.e. Lidar's capable of safely detecting oncoming traffic at 30 MPH for a passing task. From the list of acceptable components, extract the cost of each. The Phase II prototype should show proof-of-concept by applying the methodology to a use case, such as the Urban Challenge. This will include mission tasks, as well as key functions to realize this task. The key functions should be annotated with cost data, specifications and simulation components for an open-source simulator. PHASE III: DUAL USE COMMERCIALIZATION: Transition the work of phase II to a DoD development effort and potentially a homeland defense / first responder effort. Teleoperated and semi-autonomous systems are already in use for hazardous and remote missions. Improved autonomy should reduce training time and increase ease of use. Autonomous systems are also in limited use in manufacturing fabrication and logistics. One problem area is reprogramming such systems for changes in production schedule or component design. Better operator interface designs should reduce the skill levels required. Thus, wider use of autonomous systems from existing manufacturers and newly formed firms is probable. Potential commercial applications of this technology include designing autonomous processes for materials handling and or security in potentially hazardous environments.

OBJECTIVE: Develop vision-based hardware and software to enable Small Unmanned Air Systems (SUAS) to autonomously identify landing zones to enable other autonomous system teammates to land and re-launch. DESCRIPTION: Small Unmanned Air Systems (SUAS) are being developed for numerous applications, but size and weight constraints severely limit the endurance of such vehicles1, 2. Some of the missions of these vehicles could be extended by landing for certain periods, entering a low-power state, then re-launching as needed, especially in urban environments. Some technologies have been developed to provide landing gear hardware and flight controls suitable to allow autonomous landing of SUAS. A primary capability that has yet to be developed is the suite of autonomous behaviors necessary to determine when it is appropriate to land, identify a suitable landing zone (LZ), guide the SUAS to the LZ, determine when to re-launch, and re-launch. Portions of these functions could be performed by a human operator, but it is highly desirable to limit the burden on the operator in high-stress environments, or situations when an operator is responsible for multiple vehicles and functions. Therefore, a high level of autonomy is desired. One potential solution is for an autonomous team of SUAS to cooperatively perform the landing task. In this scenario, one"control-ship"would identify the LZ and guide its teammate to it from a suitable vantage point. This would give the landing vehicle the benefit of multiple perspectives of its own pose relative to the LZ. While it may vary depending on the mission, the ideal LZ would be an elevated location with clear sight lines that would allow the vehicle to continue surveillance functions in a low-power state, while also retaining some potential energy for re-launch. An example of such a LZ would be the corner of a building"s roof top. Though solutions should not be limited to this, it is expected that software algorithms can be developed to recognize relevant features of an urban environment in the SUAS"live video stream and analyze these features to identify potential LZs. The goal of this project is to begin to develop these autonomous behaviors, and demonstrate them in a simulated environment. Specifically, the machine perception, reasoning and intelligence functions of; 1) recognizing features such as roof tops, walls, corners, power lines etc., 2) evaluating them relative to over-arching mission goals to determine ideal LZ s, and 3) reasoning the optimal approach path to ensure a successful landing. PHASE I: The proposal for Phase I should develop a preliminary algorithm for identifying preferred LZ s in an urban environment using vision-based sensor data. Representative video data of an urban flight should be used to demonstrate this algorithm in a simulation in which the software would perceive suitable LZ s and output instruction to the teammate"s flight control system sufficiently to guide the simulated vehicle to a landing approach. The algorithm should be suitable for deployment on board an air vehicle, and should account for the challenges of operating in complex urban environments such as avoiding obstacles. PHASE II: In Phase II, the algorithm will be employed on a prototype quad-rotor SUAS equipped with suitable hardware and flight control capabilities to perform an autonomous landing experiment. PHASE III DUAL-USE COMMERCIALIZATION: Development of such autonomous machine perception behaviors and implementing them on a prototype vehicle will provide any potential business with valuable experience in a growing field. Commercial applications of the technology may include long-endurance surveillance missions in support of private security and community policing sectors, monitoring wildlife, detection of forest fires, etc.

OBJECTIVE: Develop an autonomous team of aerial scouts capable of cooperatively exploring an unknown indoor environment, and communicating their findings to each other and their human operators. DESCRIPTION: There are numerous applications for unmanned/robotic systems operating in complex urban or indoor environments1, 2. A high level of autonomy is desired to reduce operator workload, and vision-based navigation systems must be used in lieu of GPS. Numerous research efforts are underway to develop novel vision-based navigation systems such as Simultaneous Localization and Mapping (SLAM) in size, weight and power packages suitable for deployment on small air vehicles3, 4, 5. To maximize the effectiveness of robotic systems, cooperative behaviors between several members of a team should be developed as well. However, it is critical to ensure that as the team grows, the operator burden does not. Therefore, the goal of this project is to develop cooperative behaviors between autonomous aerial scouts, thus enabling autonomous teams of unmanned systems to perform complex missions. Specifically, it is required that algorithms be developed for cooperative exploration of unknown indoor environments by multiple small aircraft (rotorcraft, fixed wing, etc). These algorithms would allow team members to share information, including map data, team member status, and notable features of the environment, for example. These aircraft have strict payload limitations, so any proposed algorithms must have minimal computational requirements. Furthermore, the limited range and endurance of such vehicles demand that such algorithms optimize the performance of the vehicles in such a way as to maximize the searchable area for a given mission. It may be advantageous to disperse various capabilities among several specialized team members so that no individual would have the same level of capabilities of the team in aggregate. For example, certain team members could act as communication relays while others carry specialized sensors, etc. It is expected that such delegation schemes would be studied under this project. PHASE I: The proposal for Phase I should develop a preliminary algorithm that could be used by autonomous aerial robots to cooperatively search an unknown indoor environment and share relevant information with each other and human operators to improve team performance. This algorithm should then be tested in a simulated environment to demonstrate its effectiveness and feasibility for future prototyping. The algorithm should be suitable for deployment on board an air vehicle, and should account for the challenges of operating in indoor environments such as intermittent wireless communication between team members and dealing with obstructions or other obstacles. PHASE II: In Phase II, the algorithm will be employed on a team of autonomous aerial scouts and demonstrated in a representative indoor environment. Efforts should be taken to optimize the search algorithms as needed to maximize mission effectiveness. PHASE III DUAL-USE COMMERCIALIZATION: Development of such cooperative exploration algorithms and implementing them on prototype vehicles will provide any potential business with valuable experience in a growing field. There is significant potential for follow-on work in cooperative behaviors that could follow from this project. Commercial applications of the technology may include search and rescue functions in dangerous environments including earthquake rubble, avalanches, and collapsed mines, etc..

OBJECTIVE: Develop robust technologies that promote an"impedance match"or"human-IT partnership"that increases the analyst's agility and compliment the human. Traditional approaches to human-computer interaction focus on relatively simplistic human behavior (e.g., key strokes, mouse clicks, etc,). This effort will concentrate on the analyst"s experience by providing a means to address task off-loading, and adapting the workspace context based on the analyst practices and data content. This effort is accomplished by a collaboration between the human and machine, not making computers mimic people, but leveraging each of their strengths, talents and capabilities within a harmonious human-IT partnership. DESCRIPTION: Intelligence analysts currently cannot efficiently manage the amount of multi-modal (i.e. multiple file formats structured and unstructured text, video, photos, etc) and multi-lingual data available for analysis. As a result, exploitation of information contained in unanalyzed data remains undiscovered, or is delayed beyond the point where the information is no longer of any operational value. There is evidence supporting the importance of a wide field-of-view in generating a sense of immersion and presence within the data landscape. Immersion into this data landscape substantially increases human ability to navigate diverse land complex virtual environments to establish and test hypotheses. While automated processes are promising, the real-world performance of the human analyst remains the gold standard. Human errors fall into three major classes: skill-based slips, rule-based mistakes, and knowledge-based mistakes. Impaired cognitive function is most likely to increase errors involving memory, reasoning, and judgment, leading to the uncritical or biased use of faulty knowledge, hasty decisions under stress, and memory blocks that lead to unacceptable performance delays. The effects of stress, fatigue, and task overloading cumulate over time, and cognitive states characterized by errors of judgment need to be detected before serious problems occur. As information load increases, for example, people take aggressive and potentially riskier steps to manage it, such as increasing their tolerance for error, delaying analysis, shedding tasks and filtering. Performance itself is a function of task demand level and a person's ability to manage information processing. Performance deterioration associated with increasing task difficulty indicates that cognitive capacity is finite, and has been conceptualized as a single resource or multiple resources. Performance in high workload, high throughput volume tasks such as image analysis can rapidly degrade in operational settings. Ongoing reviews by analysts and researchers of the current state of R & D on how to support professionals in the Intelligence Community (IC) noted: (1) massive data overload, (2) cohort changes that include an"expertise gap,"(3) major changes in the tasks and data types that entail changes in jobs and roles, (4) paucity of truly human-centered information technology as analysts develop their own adaptations and workarounds. The proposed R & D will explore, systematically study and develop an alternative approach to our conception of the role of computers in human activity. The proposed framework proceeds from the perspective of human-computer interaction as an integrated system, and focuses on support for higher-order human activities, such as skilled performance, complex learning, and analysis. Five orchestrated phases will be employed to achieve the desired outcome: PHASE I: Studies of Analysis as Cognitive Work: modeling and studies of information analysis and comprehension as a general form of cognitive and collaborative work. Avoiding Pre-mature Closure: Issue - people using technologies to find"the"answer. Employing autonomous technologies to navigate a circumambience of graphics and images, context-sensitive data and information, and temporally/spatially-organized icons that are encoded objects representing multi-dimensional and disparate forms of data and information. Representational construct: will consist of two manifestations: 1) an egocentric display that consists of a wide-field-of-view landscape containing information and 2) an exocentric display that is a god's eye view of the entire spatial context and a tool to navigate within that space. The analyst is able to navigate within the information landscape and interact with encoded objects and terrain using a combination of natural motor control activities. PHASE II: Patterns and Broadening Checks: Designing the broadening checks needed to achieve convergence in analysis; this depends on using pattern display concepts (especially event patterns), innovations in how to take context into account, and building team work between human and machine. PHASE III: Collaborative Analysis: how to innovate means for explicit and implicit collaboration across networks of analysts; PHASE IV: Connecting AnalysisDecision MakingAction: how analysis processes and products can better support decision making, policy, and action loops such as time critical targeting; PHASE V: Integrated Analyst Workflow: how to develop and support an integrated workflow across the entire range of analyst tasks and activities.

Objective: Develop scalable computing algorithms capable of performing autonomous sense making operations based on learning and/or training. Specifically, reduce the effect of operator information overload by autonomously gathering relevant information for decision-makers and drawing meaningful conclusions from massive amounts of data, therefore optimizing human-agent interactions. Description: Future military operations will be characterized by two seemingly different, but inexorably linked problems. First, there is so much data available that the human capacity to understand it is simply overwhelmed, leading to decision paralysis. Second, technology proliferation has reduced the cost of entry to the information environment such that pertinent information can elude even the most savvy decision makers, leading to decision hubris. Military intelligence analysts must bring together multiple kinds of data: text, video, audio, images, etc. in order to glean meaningful findings to help decision-makers determine the correct military response. Today"s computing information systems are continuously bombarded with sensor, machine, and user generated data. The main challenge information analysts face today is how to triage the vast amounts of data available to ensure critical information is acted upon. As a result, this applied research topic seeks innovative ideas where emerging computing algorithms can be applied to intelligent information processing or autonomous sense making operations across multiple modalities of data. The basic goal is to develop information software platforms capable of performing autonomous, distributed data-mining that would enhance the performance, resilience, and decision making capabilities of the user by enabling autonomy within the information gathering, understanding, and presentation system itself. Phase I: Research and develop an innovative approach to meet the SBIR Topic objectives, and assess its feasibility. Develop the initial paper design and simulation prototype that demonstrate basic autonomous cyber sense making application. A proof of concept is required to demonstrate feasibility of approach. Phase II: Develop the required technologies and large-scale prototype demonstration, per the Phase I design. Develop and demonstrate simulation/emulation prototype tools and techniques for sense making and autonomous operations for example but not limited to monitoring activities, navigation in denied environments, and trends of entities or objects using performer generated real-world and/or synthesized data. A working large-scale simulation/emulation prototype is required that with increased scaling demonstrates increase in the complexity and autonomy of functionality performed by the system architecture. Dual Use Commercialization Potential: MILITARY APPLICATION: Resulting technology will deliver a scalable software tool able to fulfill a wide range of DoD needs for autonomous systems operations for example continuously monitoring information from various sources for situation awareness, information dissemination in denied environments, and robust sense making. COMMERCIAL APPLICATION: Applications in the business intelligence, persistent surveillance, automation, data and trend analysis areas for example continuously monitoring information from various sources, background screening, and DoD contractors enabling autonomous decisions and analysis.

OBJECTIVE: Develop shock, vibration, environmental and EMI-hardened energy storage enclosures that are optimized to withstand and withhold/direct the energetics of a component or cascade of energy storage component failures. To provide this enclosure and protection of nearby personnel and equipment via state of the art materials and structural design so that the volumetric and gravimetric penalty is minimized. DESCRIPTION: Energy storage systems, comprised of high-density batteries, capacitors (including electrolytic and asymmetric types) and/or flywheel technologies offer the potential for numerous benefits as applied to power systems of different types. However, high density storage systems, which may present electrical, chemical and inertial hazards must be able to be simply and effectively installed in locations which can be populated by personnel and sensitive equipment. Because of this, robust and rugged enclosures must be designed that are capable of overcoming effects related to temperature, pressure and inertial effects, at the same time. However, the approaches must provide substantial innovation because the effects upon size and weight due the enclosure and containment cannot substantially adversely affect the power and energy density of the storage systems. Innovative R & D to support the creation of compact, lightweight, and high performance enclosure structures should support the evaluation of means of enclosing and isolating energy storage systems from the surrounding environment. The overall structural approaches should be scalable so that it may be applied to small, trailer mounted systems through large shipboard-mounted systems. Approaches must be considerate of the conditions of release, including MW thermal flux from failed components, overpressures in excess of 30 psig and inertial effects of rotating machinery containing in excess of 300 MJ and 100k RPM. If necessary, the system may provide a directed ventilation approach to allow gasses generated to escape into a specific, acceptable location or direction. The enclosure shall not require substantial volume above that already taken by the storage system itself, thus an enclosure system will not expand the volume more than 10% of the racked storage components. Ultimately the design should ensure strength of the shelving, resilience to gyroscopic effects of rotating machines, and resilience to shock, vibration and environmental effects as defined in the MIL specifications provided in the appendix. Any design should be able to support devices enclosed with voltages up to 1000VDC (including arcs and plasmas) and power capabilities up to 1MW, and provide penetrations to allow cabling sufficient for moving energy in and out of the enclosure. Cooling may also be assumed to be available, but no colder than 40 degrees Celsius at a flow rate proportional to the volume of the box. It should not be assumed that copious quantities of cooling liquid are available to cool the enclosure itself, but rather the items placed inside. However, small amounts could be utilized by the enclosure itself to support internal environmental characteristics. Aspects of packaging of components internal to the enclosure could be manipulated to support the overall requirements of the enclosure system; however the design must be flexible and adaptable to specific components or combinations of components inside. PHASE I: The offeror will perform advanced modeling and analysis to evaluate the energetic characteristics of cascading chemical and inertial failure conditions, where it is assumed that a device fails on the order of one per minute continuously. The basis of the analysis will utilize the thermal and inertial metrics described above. The evaluations will be utilized to determine the requirements for scalable architectures which create minimal impact on device density. Utilizing this information, a conceptual design will be provided with traceable simulation basis to demonstrate performance. If possible, validation of simulated performance parameters will be provided prior to the option phase. PHASE II: The offeror will scale the conceptual enclosure design to relevant size, which provides dense rack-mount capability and serviceability aspects. All input and output interface points will be defined and performance simulations evaluated with a greater level of detail. Equipment will be built to the designs produced, and validation of the performance aspects (inertial, mechanical, thermal, chemical resilience) will be demonstrated. PHASE III: Design and build a full-scale flexible rack-mount enclosure system for a particular military application, meeting appropriate MIL-SPEC operational requirements. A scalable, cost-effective enclosure scheme that provides local isolation from energetic release will enable lighter, more compact energy storage to be implemented onto a greater number of platforms and operational equipment. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: This technology can be utilized anyplace that energy storage requires compact packaging and close collocation with other equipment or manned spaces .

OBJECTIVE: Develop and demonstrate a ruggedized tactical power plant generator controller to enable fuel savings and reduced generator wear. DESCRIPTION: Tactical power generation for Army deployments has demonstrated low efficiency conversion due to wide variations in load. Tactical generators routinely operate below 50% of peak power which results in low fuel efficiency. Future endeavors and deployments will require the need to operate with fuel saving technologies to reduce the logistical footprint but maintain mobility and flexibility. Generator right sizing operational analysis has shown the potential to provide the desired fuel savings and maintenance reduction while keeping a flexible, mobile overall force capability. This mode of operations involves only keeping a single generator online, up to the rated power output, to match the continuous electrical load requirement. As the continuous electrical load is increased, additional generators can be brought on line or larger single generator operations can occur. Energy storage is utilized to deal with peak loads beyond generator rating or transition to the higher continuous operational load. Fuel savings>25% are anticipated for operations in this condition. Additional supplemental incorporation of baseload renewable power can increase overall fuel savings potential beyond this. It is desired therefore to develop a tactical generator intelligent manager to enable right size operations on existing Army assets. This unit, developed under this effort, will be a form, fit, and function replacement of the existing switchbox on the AN/MJQ-18 10kW Power Plant with the development controls functionality for generator right sizing operation incorporated into it. The control unit will also maintain the capability of the existing switchbox and be environmentally hardened to handle military environments. All generator right sizing operation events will be automatic with no intervention from an operator. The system shall utilize the recently developed AMMPS 10 kW tactical generator with necessary modifications as well as energy storage and optional 3kW renewable source input. Proposals are encouraged to include generator dispatch strategies and intelligent power management concepts which emphasize the ability to manage fuel savings and generator life. The load profiles are undefined and can range from steady state loads to high peak loads. PHASE I: Develop a conceptual design for intelligent power manager controller for right size operations which will control the operation of, and synchronize power of two 10 kW generators on the AN/MJQ-18 Power Plant, an energy storage device, and an additional DC input from a 3 kW DC renewable source. The unit shall additionally be a form, fit and function replacement of the switchbox on the AN/MJQ-18 10kW Power Plant. The generator control system shall employ an intelligent power management approach which utilizes current operating conditions and intelligent dispatch strategies to manage generator operations for overall fuel savings and generator wear reduction. In addition, the unit shall have flexibility to operate with multiple communication protocols, as defined by the Army, for control of the generator units and receipt of load information from the electrical bus. PHASE II: Develop a dynamic model of the enabling capability to show how power quality is met during generator transitions. Develop and demonstrate a full scale ruggedized generator control unit to meet the needs identified in Phase I. Conduct a proof of concept demonstration to show feasibility of operation and highlight benefits of generator right size operations utilizing the latest Army communication protocols for load and unit control. PHASE III: Update the detailed design of the generator controller for a complete power unit, incorporating the technology previously developed in Phase II. Build final design unit and conduct mil-spec testing to certify for military use with a provided energy storage unit. The unit will be delivered to a military facility for demonstration testing in a relevant environment.

OBJECTIVE: Develop generic time and frequency domain analysis modeling and analysis tools to analyze and determine mitigation strategies to maintain power quality with high dynamic aircraft power systems operating with electrical accumulator units (EAU). DESCRIPTION: Ever increasing high dynamic load demands are being placed on aircraft power systems. Peak and regenerated energy demands for electrical actuation and the power and thermal management system (PTMS), electrical starting demands, and emergency power demands stress the capability of the power system to maintain power quality under all operating conditions. In order to meet these varied and complex load characteristics, mitigation strategies are required that involve energy storage and filtration for any unwanted generated distortions. However, aircraft designs remain sensitive to overall size and weight for such installations. In addition to these constraints, current options for analysis are limited in their fidelity and ability to properly examine these high transient conditions to understand how various energy storage and other devices will interact with the system and how reliably any chosen devices will perform. Therefore, a tool to facilitate dynamic time domain analysis and frequency domain analysis is desired to examine devices used to meet load requirements. During usage, the tool should have the capability to insert generic aircraft power system models including the electrical accumulator units and energy storage to perform analysis. The subsystem used for this study should be able to source a minimum of 150kW for 100ms of peak power and sink at least 150kW for 50ms of regenerated energy. In addition, the unit must adhere to, or, upon maturity, demonstrate the ability to meet MIL-STD-704F Aircraft Electric Power Characteristics. Since the available data for analysis is typically owned by the system manufacturers, small businesses wishing to pursue this topic will most likely need to partner with a large OEM. PHASE I: Develop and demonstrate concept for a dynamic time domain and frequency domain analysis tool utilizing provided aircraft operating data and models. PHASE II: Improve models as necessary to improve fidelity to identify capabilities, methodologies technologies to further improve overall power quality on aircraft power systems. Develop improved mitigation components and methodologies. Validate in a simulated power system bus incorporating signals for high demand loads, sources, and energy storage. Phase III: Validate full scale mitigation technologies in hardware in the loop ground demonstrations for future aircraft DUAL USE COMMERCIALIZATION: Military Application: F-35 and most UAV's employ electric actuation to critical flight surfaces. A measure of aircraft power system power quality is most important for these applications. Commercial Application: Emerging commercial airliners are employing utilities that are electrically driven.

OBJECTIVE: To develop compact, low thermal resistance solutions for maintaining the temperature of cylindrical energy storage components set up in high voltage arrays. Better temperature control in platforms will reduce the need to de-rate components, improving reliability and system energy density. DESCRIPTION: Future military platforms will require more extensive use of electronic power systems to achieve required performance levels. Energy storage is becoming a key enabler for power systems operating with high levels of transient power requirements and supporting fuel-efficiency operations while maintaining capability. High density energy storage which operates continuously at high charge and discharge rates to support these loads under the combination of finite space, high power and energy requirements, stringent safety characteristics and wide upper temperature ranges drive the requirement for advanced cooling systems. Proper thermal management of these high power electronic systems becomes more difficult as increasing power density requirements push heat generating components closer together. This is made more severe due to relatively low full power operating temperature limits (in the range of 55-60C for certain components) which force designers to de-rate the components to ensure reliable operation. The combination of close proximity, volumetric self-heating, and platform coolant temperatures as high as 50C all contribute to increased system volume from redundant storage arrays operating with reduced energy density. While ongoing research efforts are attempting to develop components with higher operating temperature limits, improved methods of efficiently managing the thermal aspects of batteries, capacitors and flywheel motor/generators provides a near-term reduction in the need for component de-rating which would reduce component redundancy and increase system power density. Storage devices with a cylindrical, can-style structure cannot efficiently couple to the high performance cold plates and heat sinks being implemented to cool other components. Although air-cooling methods (including finned adapters, etc.) have been utilized in the past, system volume constraints and platform placement typically remove useful convective flow paths. Other proposed techniques for thermal coupling have included the use of inefficient thermal interface materials, thermal conduction through the electrical terminals, or significant modifications, none of which provide a cost and performance effective solution. Innovative R & D is needed to investigate cooling architectures which can be enabled for cylindrical geometry energy storage components that must be integrated into larger arrays. The technologies should be scalable from small 18mm type cells through motors of 100mm or greater diameter. The cooling architectures should be able to space-efficiently couple to a backplane, being integrated into shelving or cabinet designs to help maintain climate or thermal isolation and regulation throughout the device. The innovative products brought forth through this SBIR effort should not contain precious or hazardous materials, nor require significant interfaces in order to support. It is optimal for these devices to operate such that chilled water is not required, though fresh and/or seawater can be assumed to be available at 40C, with sufficient flow available to meet mission needs. Ambient spaces should be assumed to be up to 60C and worst-case device maximum temperatures should also be assumed to be 60C. In order to maintain density of the energy storage devices, these cooling structures should not expand the individual components greater than 10%, yet be capable of supporting pressure both from the inside in the form of cooling fluid flow, as well as external under compression of a battery pack, expansion during cycling operation, etc. If a special cooling fluid is to be utilized, the interface to facility/platform cooling fluid should be considered, along with the impact on efficiency and device/system density and packing factor. PHASE I: The offeror will determine the feasibility of cooling devices through advanced architectures which are scalable and create minimal impact on device density. Proof of concept will be shown on a synthetic scale via modeling and simulation with comparison against current methods. Demonstration of the proof of concept will indicate maintenance of temperatures in a cylindrical geometry with resistive heaters as a source. The proof of concept should also be demonstrated on operating storage devices to compare to control of the same design. This should be a high power operation, with all safety considered as part of the determination of size. PHASE II: The offeror will scale-up the cooling in applicable geometries for arrays of cylindrical devices operating together with a minimum individual component diameter of 26mm. For batteries or super-capacitors, the design should be strings no less than 48VDC. Evaluate the performance and limitations of the prototype for a range of coolant temperatures and internal/external thermal conditions. Validate through modeling or demonstration the ability to transition the solution for specific military applications and characterize any array dependent size/performance trade-offs inherent in the solution. PHASE III: Design and develop a modular cylindrical structured cooling mechanism for a particular military application, meeting appropriate MIL-SPEC operational requirements. Continued commercial investment in hybrid ground and air vehicles will increase the demand for larger power conversion systems. A scalable, cost-effective capacitor cooling scheme will enable lighter, more compact electronics solutions to reach the commercial market. PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: This technology can be utilized by the mobile construction industry to save fuel costs and reduce emissions. State and local governments could also benefit from this technology for various applications.

OBJECTIVE: Demonstrate cost effective, secure, militarized power line communication system components which can provide a reduced infrastructure solution to properly manage variable resources and loads for multi generator operations, bases, and platforms. DESCRIPTION: Communication is the key to successfully managing the elements that make up a smart electrical architecture or grid including components such as generators and energy storage. The communication system should provide real time determination of operational allocation as well as individual asset state of health. An optimized communication system can also seamlessly integrate renewable power sources that can be distributed throughout the base even if portions of the micro-grid are damaged. Smart micro-grid management and power source distribution decreases the likelihood that critical mission activities could be without power. However, the overall complexity, cost, and time associated with installation, maintenance, and operation of current communication options such as fiber optic or coaxial cable can be excessive. These current methods require installing a dedicated communication cable or using a wireless system. However, the wireless system could potentially interfere with other RF (Radio Frequency) systems. The method under this effort would use power cables as the communications backbone thus reducing the cost of installation, maintenance and interference with operations during installation. This method would also reduce the cost of running a dedicated fiber or coaxial line throughout the installation to handle the communications function as well as significantly reducing install and setup times. The technology gap between commercial state-of-the-art devices capable of sending information over the power cable and the type of device necessary to intelligently manage a smart micro-grid for cost effective military purposes is desired. This effort is designed to bridge that gap with a combination of affordable modified devices capable of sending and receiving data over the power line including power sensors, power electronics, smart power distribution hardware, etc. PHASE I: Provide a conceptual design of a power line communication system to properly manage a smart electrical grid utilizing multiple generator sets, energy storage, operating on a 208VAC, 3phase electrical bus. This conceptual design should account for proper communication protocol and latency, security of operation from outside sources, security of operation and power quality from interference derived from grid operation resources and loads. In addition, consideration should be given to characterization of the communication channel, modulation techniques, and considerations for noise on the channel. Emphasis should be given for ease of installation, operation, maintainability, and cost effectiveness of components including devices capable of sending and receiving data over the power line including power sensors, power electronics, smart power distribution hardware, etc in a military environment. Provide a conceptual design of individual militarized critical components as required. PHASE II: Provide a detailed design of critical cost effective military power line communication components. Perform a scaled demonstration of critical components utilizing PLC to prove feasibility of qualities outlined in Phase I description. PHASE III: Pursue dual-use of the developed hardware solution. There are three areas where the technology development in this SBIR proposal could prove dual-use.

Objective: This topic seeks proposals to develop innovative, human-in-the-loop mechanisms for identifying analysis, test, and evaluation workflows that need to be generated in order to reduce risk of unanticipated effects. Description: The Department of Defense (DoD) must be prepared to support a wide range of missions across dynamic and uncertain futures, including rapid changes in missions, threats, and operating environments. This requires efficiently creating, fielding, and evolving trusted defense systems that can proactively meet these needs. A key goal is ensuring that engineering programs maximize utility and represent best value for the investment across a breadth of potential missions in joint operational environments. Design, analysis, and assessment of multiple alternatives to achieve this goal requires that internal and external interactions with the design solution be well understood, and implications of both design and usage choices be communicated to the decision maker. Therefore, new methods and tools are needed that compensate and recover from disruptions, adapt to dynamic environments, and rapidly deliver new solutions. In a non-complex system, components can be modeled in a simple way and their interactions are governed by well-defined deterministic rules. The overall behavior becomes predictable to a high degree of accuracy. Conversely, complex systems are dynamic, have non-linear responses, are sensitive to initial conditions, learn and adopt behaviors such that they are not predictable, and interactions amongst components are not governed by well-defined rules. Consequently, knowledge of individual components in a complex system reveals little to nothing about system behavior, which makes overall behavior difficult to accurately model. The fundamental consequence of cross-domain interaction is that the components integrated into systems no longer behave the way they did in isolation. The fact that components change behavior when integrated into systems means that systems must be designed together with components, and designs of components must be rechecked at the system level. Phase I: The Phase I is expected to develop sets of design analysis, testing and evaluation workflows that will identify risk pathways of unanticipated effects. Phase II: The Phase II is expected to reduce sets of these workflows to optimal subsets for decreasing risk under conditions of time and cost bounds on total effort expended. Phase III: The Phase III is expected to ensure executability of workflows from multiple tools or databases that use differing schemata, hierarchies, and attributes by intermediating between outputs of workflow elements and inputs of their successor elements.

Objective: Develop methods for allocating system functions to implementations of hardware or software with the intent of quantitatively assessing the benefits and drawbacks of these allocation options from a hierarchically increasing view (component, subsystem, system, system-of-systems). The desired outcome is a method for making comparative assessments and design trades between allocation of the same function to hardware and software implementations. Description: The Department of Defense (DoD) must be prepared to support a wide range of missions across dynamic and uncertain futures, including rapid changes in missions, threats, and operating environments. This requires efficiently creating, fielding, and evolving trusted defense systems that can proactively meet these needs. A key goal is ensuring that engineering programs maximize utility and represent best value for the investment across a breadth of potential missions in joint operational environments. Design, analysis, and assessment of multiple alternatives to achieve this goal requires that internal and external interactions with the design solution be well understood, and implications of both design and usage choices be communicated to the decision maker. Therefore, new methods and tools are needed that compensate and recover from disruptions, adapt to dynamic environments, and rapidly deliver new solutions. System lifecycle cost, reliability, availability, and other measures for assessing the effectiveness of a system are iteratively and recursively constructed from the component level up to the system or system of system level. When functions are decomposed and allocated at each hierarchical level, system designers need to decide whether a function is better met through an implementation of hardware or software. The impacts of these decisions need to be understood earlier in the design process in order to limit or eliminate delays and non-compliances of the system. Traditional values associated with software include increased execution speed and scalability, while hardware is associated with integration, testability, modularity, and lower cost. With the expanding role of software in achieving system functionality, the aforementioned values are no longer binned into either implementation. The result is the ability to more evenly trade the implementation of hardware or software. New methods and tools are therefore needed to make early, informed trades between allocation of functions to hardware or software and understand the system-level impacts of these decisions. Phase I: The Phase I is expected to develop approaches for manually and automatically allocating functions, at varying levels of the system hierarchy, to hardware and software alternatives and determining the optimal implementation allocation approach based on a set of quantitative measures of benefits and drawbacks (reliability, safety, function execution speed, reusability, lifecycle cost, etc.). Phase II: The Phase II is expected to produce a demonstration of capability for the allocation and assessment environment at the component and subsystem level. The selected measures of utility will be quantified, baselined, and compared for various combinations of hardware and software selections. Phase III: The Phase III is expected to produce an expanded capability, demonstrating the applicability of the method, and human-aided allocation selections and assessments, up to the system-of-system level.

Objective: Develop and demonstrate novel decision support concepts and supporting Human Computer Interfaces (HCI) for the supervisory control of multiple autonomous systems, concurrently engaged in multiple missions. Description: The next generation of unmanned platforms for Navy missions must be capable of autonomously responding to multiple, competing needs that will arise dynamically over the course of a missions. As described in [1], achieving fully autonomous systems will require the system to transact on the basis of goals and translate them into a series of tasks to be performed without extensive human interaction. These tasks may need to be revised over the course of a mission as a function of unanticipated mission events, or emergent mission needs; yet within the constraints established for safe operations. A human will still need remain in the loop in supervisory capacity. However, during an emergency or with an urgent change in mission goals, there may be significant time delays before human intervention occurs. Therefore it may be assumed that these systems have high levels of autonomy that operate through a sophisticated supervisory control model. This topic is intended to develop innovative solutions for the unique requirements of operating highly autonomous systems. This need is critical due to the urgent need to move to a single controller being able to monitor and control multiple unmanned systems and performing across domains as collaborating teams, as documented in numerous Department of Defense objectives [2]. The ability to efficiently managing multiple goals for autonomous systems will be a key enabler to reducing the operator-to-platform ratio. The current state-of-the-art in autonomy mission management technology is limited to relatively few pre-specified goals and very little to no capability to adapt the goals without significant off-board (human) mission planning. In contrast, the next generation of autonomous systems will attend to multiple changing goals arising from a rapidly changing situation on the ground. Methods and algorithms are required to enable an efficient interface of mission goals and tasks for the management of multiple software agents or platforms. This will provide the human planner the flexibly to redirect the platform and reduce the number of specialized operators needed for platform management. In particular, methods are required for mixed-initiative goal formulation, goal prioritization, goal retraction and goal-based agent autonomy. The desired interface research should address a modular architecture that describe: 1) Reasoning approaches for mixed initiative goal management in multiple and concurrent,( but potentially related), missions; 2) An ontology for mission types, tasks, and plans with approaches for integration to the efficient display and manipulation of mission essential tasks; 3) A notional ontology for autonomous platforms capabilities and capacities; and 4) Knowledge authoring tools for autonomy management knowledge bases (e.g., the ontologies themselves). Phase I: Develop an initial design that considers appropriate human factors design principles in providing an intuitive interface for managing mission goals in real-time to enable the user to rapidly adapt to changes in mission context (Note, reports on interfaces for autonomous platforms are generally geared toward the engineer who has great insight into how the systems works, and not the operator who may be focused on meeting mission objective and may not care how the internal system works [3]. This design specification must address functionality from the human supervisor perspective); a workflow that is able to recommend sequences of goals that should be pursued to meet mission requirements; and definition of metrics to assess the improvement in overall system performance against a baseline capability. Phase II: Develop a detailed design and implement an advanced prototype of the autonomy management platform. Demonstrate the effectiveness of autonomy management in multi-mission operations in hostile or adversarial environment and derive and empirically demonstrate improved operator responsiveness to real-time changes in mission execution and/or changes in goals. This phase should involve: specifications for how the technology would integrate with an actual autonomous platform and rationale for selection; rationale for the selection of the appropriate open source software platform; documentation of the Concept of Operations for representative sets of missions; documentation packet for the Institutional Review Board to enable experimentation with Subject Matter Experts; and data collection and analysis techniques to support the evaluation of metrics defined in Phase 1. Lastly, develop a transition plan. Phase III (PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL USE APPLICATION): Refine the prototype and make the feature set complete in preparation for transition into the Navy. In addition to the Department of Defense, there will be a considerable demand for managing goals associated with autonomous systems in the commercial sector, federal and state agencies such as law enforcement and emergency management. For example, research and development in the field of robotics in the entertainment industry and home service industry can benefit from goal management techniques. At the state level, local police agencies now use lightweight unmanned systems to support surveillance, and as these systems become more complex and gain wide spread use, the ability to manage goals for different law enforcement missions will be required.

OBJECTIVE: Develop and demonstrate effective teleoperator interface methods for supervisory control of a network of assets in a fully-immersive, synthetically-augmented environment. DESCRIPTION: Surveillance and intelligence gathering can be linked to finding needles in haystacks it may take days or weeks to gather enough evidence in an operational environment to take decisive action. If non-cooperative subjects know they are under surveillance, enough evidence may never be aggregated; this is particularly true in urban environments where it is difficult to observe a scene without being noticed. To enable intelligence, surveillance, and reconnaissance (ISR) operations, smaller and stealthier sensors are currently being developed that will allow the operator to gather critical information remotely. This physical detachment from the combat arena requires revolutionary interface technologies to provide the operator with a sense of presence in the remote environment. Obtaining a sense of environmental presence is critical to the operator"s decision-making ability, situation awareness, and workload. In order to gain an authentic sense of environmental presence, research suggests that a fully-immersive, synthetically-augmented display system is ideal for contemporary sensor operations. An example of such a system is the Supervisory Control and Cognition Branch"s (RHCI"s) Immersive Collaborative Environment (ICEbox), which employs several large, high resolution displays, appropriately positioned to completely enclose the operator. In contrast to traditional sensor display systems, which consist primarily of an assemblage of several computer monitors, the fully-immersive characteristic of the ICEbox, as well as its ability to exhibit synthetically-augmented overlays, perceptually allows the operator to establish a sense of presence in the remote environment. It is believed that by developing a perceptual sense of environmental presence through an expansive field of view and data presentation the operator will experience an improvement in both decision-making and situation awareness, and a decrease in workload. Despite the identified benefits related to remote interaction with a network of collected assets, employment of such a system has yet to come to fruition because the state of the art of fully-immersive, synthetically-augmented display technology suffers from a deficient means for human-machine interaction. The most significant challenge faced by researchers and designers is developing an innovative and effective human-machine interface that does not rely on the impractical (in this instance) use of a traditional keyboard and mouse. Ideally, since humans routinely exercise several sensory avenues simultaneously during information exchange, an intuitive human-machine interface would consent to the use of multiple modes of input concurrently. However, recent efforts at incorporating alternative modes of input (such as speech recognition, touch, full-body gesture recognition, eye-tracking, etc.), are commonly focused on the use of a single modality at the exclusion of others. Additionally, inaccuracies related to the recognition of user input, and the interpretation of user intent, continue to arise with each individual modality. Furthermore, for those few who have attempted to fuse simultaneous input from multiple modalities, the noted problematic occurrences are compounded by the complexity of the integration. As a result, the state of the art of fully-immersive, synthetically-augmented display technology is handicapped from progressing at a more accelerated rate. If fully-immersive, synthetically-augmented display systems are to rapidly mature, a more robust and naturalistic means for human-machine interaction (HMI) must come to fruition. Creating HMI that realizes this"telepresence"and mission effectiveness simultaneously requires developing novel interface concepts, which in turn will distill requirements for sensor and information network responsiveness and control. Actualizing this capability by means of a human-centric approach should improve operator performance at both the individual and team levels. PHASE I: For remote sensor network management, develop a framework that supports intuitive human-machine communication in a fully-immersive, synthetically-augmented environment. Demonstrate aspects of the constituent technology and illustrate how it will be incorporated to provide enhanced benefits in Phase II. Develop an experimental plan to establish improvements in usability in Phase II. PHASE II: Develop and demonstrate a prototype system to be employed in a representative application domain simulation. Evaluate the human-machine exchanges to illustrate payoffs in interaction speed, error reduction, workload, training time reduction, and/or interaction flexibility. PHASE III: MILITARY APPLICATION: Successful maturation of intuitive human-machine interaction technology to be employed in a fully-immersive, synthetically-augmented environment would enhance a variety of complex military and commercial monitoring, planning, and control domains. COMMERCIAL APPLICATION: Remote sensor operations and RPA control are immediate application areas, but utility would also be warranted in domains such as virtual learning environments, advanced business teleconferencing, and enhanced medical imaging systems.

OBJECTIVE: The objective of this effort is to develop and demonstrate a minimally intrusive technology that supports gesture recognition for safe control of UASs on a carrier deck during flight operations. DESCRIPTION: Future concepts of operations for Unmanned Aerial Systems (UAS) include a requirement for an aircraft carrier based platform. A major challenge with basing UAS on aircraft carriers is integrating them into the flight operations launch and recovery cycle the sortie rate. Currently, on a carrier deck, control of manned aircraft is achieved primarily through gestures presented to aviators by Sailors tasked with handling and directing aircraft. These personnel use a well-defined, and minimalist, set of gestures to guide aircraft through a range of deck-based maneuvers. The use of non-traditional commands- or non traditional approaches to deliver these commands- to maneuver UASs on the flight deck will introduce significant hurdles in terms of integrating UASs into flight operations and may significantly increase a carrier"s sortie rate a key readiness measure. Recent advances in several technical domains provide a unique opportunity to develop the capability for UASs to recognize and act on the same kinds of gestures that are used to guide manned aircraft. These include: the ability to support human gesture recognition without requiring the user to wear special equipment [1]; The development of knowledge structures to provide a formal approach for representing and characterizing underlying behaviors [2,3]; and, advances in developing systems that can communicate naturally with human users [4]. Along with other technologies, these advances provide the necessary framework for providing carrier-based UAS platforms with the capability to recognize the same or similar- set of gestures used to guide manned aircraft around the flight deck safely and effectively. This topic is requesting proposals for developing Natural Dialogue based Gesture Recognition for Unmanned Aerial System Carrier Deck Operations. Specifically, it is requesting technologies that will: Allow for the recognition and understanding of the complete set of gestures used to guide manned aircraft during carrier deck operations; Accomplish this recognition in the same duration that is required for guiding manned aircraft Require only minimal additional devices to be used by the individuals making the gestures Demonstrate initial accuracy of at least 80% Have the ability to increase accuracy rates through learning Have the potential to learn new gestures PHASE I: Required Phase I deliverables will include a feasibility study for a Natural Dialogue based Gesture Recognition for Unmanned Aerial System Carrier Deck Operations that can provide a simple, easy to use ability for guiding UAS on a carrier flight deck without negatively impacting sortie rate. Included in this study will be an initial concept design, as well as a detailed outline of success criteria that address the requirements listed in the topic description. A final report will be generated, including system performance metrics and plans for Phase II. Phase II plans should include key component technological milestones and plans for incremental and final test and evaluation. Phase 1 should also include the processing and submission of all required human subjects use protocols should these be required. Due to the long review times involved, human subjects research is strongly discouraged during Phase 1. PHASE II: Required Phase II deliverables will include the construction, demonstration and validation of a prototype based on results from Phase I. All appropriate engineering testing will be performed, and a critical design review will be conducted to finalize the design. Additional deliverables will include: (1.) a working prototype of the system (2.) drawings and specification for its construction, and (3.) performance assessment based on tests conducted in one or more simulated and/or operational settings, in accordance with the success criteria developed in Phase I. Assessment should include measures of system performance as well as human factors/usability-like measures of user performance. PHASE III DUAL USE APPLICATIONS: This technology will have broad application in commercial as well as military settings. The US Navy continues to aggressively pursue a carrier based UAS capability. The proposed technologies will provide a significant benefit in realizing this goal. Other services continue to develop ground based unmanned systems which, likewise, should benefit from replacing more traditional and cumbersome control interfaces with the natural interfaces proposed in this topic. Commercially, Unmanned Systems are finding increase use across a range of applications including crowd monitoring, border patrol and related activities. These applications require often times require a rapid response by a single responder. The proposed technologies in this topic will provide a much reduced technology footprint, thereby making single responder requirements more manageable.

OBJECTIVE: Develop innovative tools and techniques that aid in a formalized, Cyber-based forensic evaluation and assessment of a System under Test (SuT). DESCRIPTION: The U.S. Air Force Command and Control (C2) core function must address the increased necessity to develop and employ proper testing and evaluation capabilities for high-fidelity security assessments of their SuT. The analysis of overall system performance and behavior prior to implementation and transition activities is leading to organic, cyber-centric system testing methodologies, which can lead to volumes of data that must be evaluated with manual processes and system-level tools post exercise. Deriving accurate qualitative and quantitative measurements of a designed system while informing the level of composition"goodness"(e.g. toward a quantitatively-measurable certification and accreditation activity), regardless of intended domain or system-level implementation, is based on many contributing factors. To produce definable and repeatable performance metrics, technologies are needed to aid in gauging attack success, interdependent of attacker starting point or level of authorization to the underlying system. A reasonable quantified output should be in a form which can not only measure the success of the attack but also may lead to improvements in components of the SuT attack vectors and the eventual implementation of potential mitigation strategies. This should help lead to a concrete measurement to the of the attack and attack effects based on a functional requirement to prove or disprove the basic claims of the system, while capturing system-level sustainability, any potential degradation of service, and likelihood of future attacks. Analysis of observed events across multiple client sessions and correlation between collection points requires a focused solution. Adherence to testing and evaluation Rules of Engagement (RoE) should not negatively impact the collection of test data. Multiple attacker starting zones with potentially varying degrees of privilege escalation can make log capture and real-time, dynamic awareness of the attack effects challenging. Coverage against multiple classes of attacks and the distributed nature of today"s services-centric systems warrant measurement at the physical network level to properly characterize the physical attack effects at the node and network layers. Measurement of undue stress at the CPU or network layer should inform system orchestrators and administrators based on best-practice composition techniques and acceptable thresholds of the system. Understanding and validating adversary location through various checkpoints, primary and secondary decision points, and situational analysis may help capture any unforeseen consequences of attack on any attack vector within the system and lends to a repeatable measure of success and failure. In this research, we seek to research, augment, and develop cyber testing and evaluation analysis tools that incorporate best practice concepts where services-based systems can be designed and evaluated for performance and behavior characteristics. Other desirable features of this toolkit might include the ability to dynamically capture the efficacy of information technology assets under test, run simulations over heterogeneous system models, generate reports supporting testing and certification activities, and perform change impact analysis on existing systems using the models that are specified for them. PHASE I: Describe and develop an open framework, techniques, and tools for the aggregation, visualization, and dynamism of a combination of specific logging functionality, network and node behavior, and starting point to aid in the real-time security assessment of heterogeneous nodes and diverse network topologies to include cloud-based assessments. PHASE II: Develop, implement and validate a prototype system that leverages the capabilities of Phase I. The prototype should produce adequate representation of the factors in attack; give proper coverage of the target environment and the functional goals for the assessment. Accuracy across multiple runs should yield a solution the can be replayed in real or near real-time to aid security members from multiple teams (i.e., Red/Blue/White) easing capture of the assessment of the security claims of the system and ensuring the safety of military operations. PHASE III DUAL USE APPLICATIONS: The value of assessments of distributed systems helps assure critical mission technologies. A standardized form of automated evaluation of the critical components of a security assessment without conducting manual log and data gathering post-exercise will allow security assessment teams and system designers the capability to alter testing specifics based on a better, more focused representation of the state of the system during attack. This will allow members of the defense industry and the commercial domain to conduct a more thorough evaluation of their existing systems and will result in a visualized-awareness regardless of the order of magnitude of the logging. Focus on log interpretation and integration from disparate sources has the potential to become a central service in the civilian domain where a real-time situational awareness at the system level is mandatory.

Objective Develop a framework and infrastructure to enable system reasoning for supporting self-diagnostic, adaptive systems toward achieving missions. Description This SBIR topic solicits the design and development of framework, infrastructure, knowledge/model representation and methods for runtime reasoning of system"s state, integrity and security of software systems, including operating system, applications and their components. Large and complex systems of software, such as the ones used by DoD, are difficult to completely verify and secure. These systems are vulnerable to compromises which take advantage of the architecture, protocol and implementation weaknesses and flaws. As breaches and compromises have become a fact of computing life, it is important that our computing systems can adapt and operate effectively under such conditions. There is a need for a system which can continuously assess its own state/health, capabilities and limitations, and adapt to the situation, at cyber speed, toward maximizing the potential success of the missions. In the heart of such system there is a comprehensive and timely system reasoning infrastructure, a data-acquisition/event-recognition system and an intelligent system controller. Continuous reasoning and assessment prescribes the use of lightweight, programmable and selective data acquisition methods, which dynamically probe the system for various set of data points over time, under guidance of the system controller. If recorded, a log provided by the data acquisition system is contextual and sparse. The system controller uses system reasoning infrastructure to make sense of the probed data, compute the future event of interest and translated them into detailed description which can be used to set up the data acquisition system to catch future events of interest. Current approach for analysis and diagnostic of a software system is based on ad-hoc rule/knowledge, provided by experienced practitioners. Such a static rule/knowledge based system cannot provide a comprehensive and up-to-date knowledge/model of the system. It is relatively inflexible and cannot account for unexpected conditions and new problems arising from removal, installation and changes of systems"components. Ad-hoc knowledge is also relatively slow to adapt, and requires time consuming manual analysis and knowledge update. Other form of diagnostic is the computer forensic or other type of memory-dump/system-image analysis. High frequency memory-dump based data acquisition can be prohibitive in term of load on the processor and data bus, and the forensic process is often heavy, requires manual intervention and will not be timely or responsive enough for runtime deployment. This solicitation emphasizes on the design and development of the framework, infrastructure, and exploration for potential representation and methods for runtime (real-time or semi-real-time) reasoning of system"s state of software systems, including operating system, applications and their components. The system reasoning infrastructure solicited in this topic needs to: 1) provide the appropriate abstraction for efficient reasoning, as well as providing bridging into fine grained semantic where an event or a sequent of events can be described and recognized/captured during runtime, 2) enable automated iterative analysis/diagnostic process, and predict/compute event(s) of interest based on its understanding of the systems"state, 3) accommodate the systems"configuration dynamic, as the components of the systems are removed, installed, or upgraded, over time, 4) and if necessary capable of analysis/diagnostic based on contextual and sparse log of dynamically (in term of instances and time) monitored events and parameters. Phase I Design and develop a framework, infrastructure, and methods for runtime reasoning of system"s state, integrity and security of software systems, including operating system, applications and their components. Develop a proof of concept implementation of the proposed design to demonstrate its required functionalities. Phase II Develop and demonstrate a prototype framework, infrastructure, and methods for runtime reasoning, on a simple or pared-down operating system and a set of applications. Develop test cases and demonstrate that the system reasoning infrastructure satisfy all of the required functionalities. Phase III Dual Use Application This overall system could be used in a broad range of environment requiring resilient computing infrastructure. It is applicable to both military and civilian enterprise applications.

OBJECTIVE: Developing cyber security metrics and algorithms for measuring resilience and mission criticality of cyber assets in wired and wireless networks. DESCRIPTION: Cyber assets usually support missions with different priorities, and the objective and systematic measurement of their resilience and criticality may play a major role in mission success. One basic requirement for achieving mission success and mitigating the adverse impact of advanced threats is to measure the defense and resilience effectiveness of individual and collective cyber assets. Therefore, a comprehensive framework of metrics should be developed objectively and systematically for measuring the individual and collaborative resilience and mission criticality of cyber assets by taking mission assurance into consideration in wired and wireless networks. This framework should be modular in nature to account for the impact of different types of advanced threats and vulnerabilities, be reactive to network connectivity failures and new threats, and provide the commander with a status of metrics of interest on resilience and mission assurance. PHASE I: This would develop/leverage a cyber measurement framework and set of metrics that are suitable for measuring resilience and mission criticality of cyber assets in a tactical environment. The investigator will develop algorithms to solve the aforementioned problem of measuring resilience and criticality of cyber assets by taking their individual and collective contribution to the overall mission success in a network environment where cyber assets belong to different command levels and/or multiple missions of different priorities. The Phase I should show the initial concept design of measurement framework as well as modeling key elements of resilience, asset criticality, and mission assurance for various scale of CNDSP operations. An integration design and experimental plan for cyber measurement framework is sought in this phase. This plan identifies necessary performance goals of interest in measuring resilience and criticality of cyber assets for mission success. PHASE II: Execute the Phase I design plan. Develop, test, and validate implementations of top contending algorithms from Phase I. Show progress with initial performance goals and show appropriate milestone to extend these goals to a desirable military operational state. Demonstrate framework in a controlled laboratory environment at a minimum with potential for field demonstration in an existing CNDSP operational networking environment. PHASE III DUAL USE APPLICATIONS - Military: It is intended that these metrics, algorithms, and associated implementations be transitioned to ARCYBER for operational deployment. It is intended that a Phase III is encapsulated in a capstone demonstration at TRL that exceeds TRL 6. - Commercial: The resulting metrics, algorithms, and associated implementations should have wide applicability to commercial network defense and network monitoring organizations or groups. The metrics and algorithms will have great potential use in the R & D community as a research tool.

with performance acceptable for operational deployment. DESCRIPTION: Existing CNDSP alert generation tools are based on the identification of known signatures and thus are not appropriate for the detection of advanced persistent threats in which the attacker explicitly avoids the use of known signatures. This leaves analysts with the time consuming process of analyzing raw data to identify such advanced persistent threats or leaves detection until after the attacker"s compromise exhibits identifiable external behavior. While anomaly detection techniques have been examined by the research community, their TP and FP rates have typically left them undeployable. The need is to achieve true positive (TP) and false positive (FP) rates in non-signature based techniques that are deployable, i.e., high percent of TPs and very low number of FPs in relation to the number of TPs. PHASE I: Develop approaches to solve the aforementioned problem of non-signature based anomaly detection with high TP and low FP rates on full packet analysis. The performer will develop detailed analysis of predicted performance that validates the TP and FP positive rates will be acceptable for deployment in large-scale CNDSP operations. The Phase I must show the initial concept design as well as modeling of key elements to support the aforementioned validation results. A design plan identifying the progression from theoretical approach to prototype and full development along with testing and validation protocols must be developed. PHASE II: Execute the Phase I design plan. Develop, test, and validate implementations of top contending algorithms from Phase I. Show progress with initial performance goals and show appropriate milestone to extend these goals to a desirable CNDSP operational state. Demonstrate framework in a controlled laboratory environment at a minimum with potential for field demonstration in an existing CNDSP operational networking environment. PHASE III DUAL USE APPLICATIONS - Military: It is intended that these algorithms and associated implementations be transitioned to CNDSP groups for operational deployment. It is intended that a Phase III is encapsulated in a capstone demonstration at TRL that exceeds TRL 6. - Commercial: The resulting algorithms and associated implementations should have wide applicability to commercial network defense and network monitoring organizations or groups. The algorithms and performance metrics will have potential values to the R & D community as an indication of future research directions and the potential for solving the true challenge problems in the cybersecurity domain.

OBJECTIVE: Develop an indications and warnings threat feed aggregation with weighted scoring to provide DoD with (near) real-time information on adversaries, to include forewarning of enemy actions or intentions DESCRIPTION: Indications and Warnings (I & W) are intelligence based activities that are intended to detect and report on time-sensitive intelligence information of foreign developments that could involve a threat to the United States military, political or economical interests. Computer Network Defense Service Providers (CNDSPs) throughout the DoD are required to facilitate situational awareness of adversary cyber actions and intentions, but the capability is minimal. Situational awareness is imperative to providing effective computer network defense and securing the DoD Global Information Grid (GIG). PHASE I: Design an aggregated feed of known threats associated with ASN, CIDR blocks, and inheritance based on association and communication internal and external to the DoD GIG. PHASE II: Implement the design from phase I and define an algorithm to weigh individual threats based on source, volume, and association. The capability should provide DoD with situational awareness of cyber threats and adversary behavior, in order to target cyber defense operations. PHASE III DUAL USE APPLICATIONS - Military: Establish correlation of threat information to internal data, such as flow records, systems logs, firewall logs, etc., but information on threats found internally may not be released external to the DoD. The system may be setup as a"shadow"server in order to replicate threat data for correlation and trending based on internal data, while also ensuring the data stays internal to DoD. - Commercial: Operational systems may be located anywhere from a cloud-based login (external to the customer) or internal appliance which correlated threat information to internal customer data, such as flow records, system logs, firewalls logs, etc.

OBJECTIVE: Develop a model of threat identification coupled with a means to redirect, reroute, or otherwise dynamically divert suspicious or malicious network activity to independent locations for investigation, in order to create a resilient network boundary capable of handling potentially widespread attack vectors. DESCRIPTION: The current model implemented by Einstein for Trusted Internet Connections (TIC) services for the detection and mitigation of the Global Information Grid (GIG) enterprise is often inefficient and ineffective based on the volume of legitimate, suspicious, and known bad traffic. By creating a dynamic system of threat identification with an ability to control traffic and redirect as necessary to disparate locations, DoD will be capable of performing organized analysis of advanced persistent threats. Using a protocol such as BGP FLOWSPEC to allow internal analysis of both internal and external information to directly manipulate the path of network activity across a wide area network (WAN) will provide targeted analysis leveraging specific algorithms. Not only does the method enhance detection capabilities, but also analysis and algorithms to be applied to a smaller, more manageable data set. An operational solution would be a combination of protocols, architecture, and analytical locations where data is sent. PHASE I: Define a plan to develop a threat feed, internal traffic monitoring, and correlation of system aggregation into rule sets. Included in the plan will be a connection of the threat feed systems with boundary network devices using BGP FLOWSPEC, which will determine the necessary human intervention versus automation. Also, define the optimal network architecture for maximum performance (division of threats, placement of filters and redirects, etc.) PHASE II: Implement Phase I plan and further define mitigation strategies, such as redirect passive, redirect active, man-in-the-middle, or black hole. Potentially develop a solution to covertly redirect the traffic, consisting of packet manipulation, and hop count using IPv6. PHASE III DUAL USE APPLICATIONS - Military: With a military implementation, details of the employment of BGP FLOWSPEC enabling traffic resilience will be sensitive to DoD. A similar architecture and capability may be implemented and designed for the requirements of DoD to provide the data in the appropriate places to effectively and efficiently analyze adversarial behavior. - Commercial: The system could be employed to route traffic for analysis to commercial locations interested in further analyzing threats posing their organization, which would be beneficial in prioritizing defense mechanisms and responses to the activity.

OBJECTIVE: Develop an innovative cognitive knowledge-aided information processing technique to take very large intelligence data streams over wide areas and autonomously highlight areas of interest for the image analyst without a priori knowledge of the area and/or location of targets of high interest. DESCRIPTION: To meet the Department of Defense"s need of advancing surveillance and reconnaissance for predictive intelligence, there has been a trend towards increasing the capability of data collection devices to deliver high resolution data points over long periods of time. Rapid advances in wide-area surveillance technologies result in exponential growth in data covering hundreds of square kilometer areas. Significant improvements in computational throughput from digital signal processors, such as the Field Programmable Gate Array (FPGA) and the General Purpose Graphics Processing Unit (GPGPU), have resulted in the processed data getting to the image analysts in real-time. However, this exponential growth in data volume means that human operators must very quickly make informed decisions on sub-meter resolution products covering hundreds of square kilometers of area. Development of autonomous information processing and cognitive knowledge-aided processing frameworks designed to do the following will greatly aid image analysts: 1. Increase situational awareness of the human operator 2. Reduce the manpower necessary for imagery scanning 3. Assist and enable decision makers responsible for resource allocation decisions that must be informed in real-time by the incoming data stream Future airborne sensor systems that collect data will be able to be information processing centers as well. For example, one such system will be capable of dual-band (UHF and X-Band) synthetic aperture radar (SAR) that will be used for persistent surveillance of an urban area 10 km x 10 km with 1 ft resolution at X-Band and fully-polarimetric 1m resolution at UHF in the nominal operating mode. The radar is software configurable and can easily be switched to one of its many other operating modes. Image analysts can be easily overwhelmed with the gigabytes/sec data stream. The research required for this effort will be to develop an intelligent and autonomous method to use the information from the data products for finding areas of interest to focus the human operator"s attention. These areas of interests will be presented to the human operators for threat assessment. The human operator may decide that collecting data in a different operating mode will better illuminate targets in the areas of interest. The sensor system can then be easily re-configured for that purpose and will provide better data for evaluation by the processor and human operator. PHASE I: To investigate methods of using SAR information data products (imagery, GMTI data, change detection, etc.) as inputs and building an autonomous sensing and deciding framework to highlight areas of interest for the human analyst. Phase I will research technologies and develop design concepts for a novel and feasible approach that focuses the attention of the analyst. More specifically, we are looking for scalable models and algorithms that can achieve real-time performance of one SAR frame per second. The targeted SAR frame size is 20,000-by-20,000 pixels (12-bit binary data per pixel) with resolution of one foot. The targeted GMTI data contains a list of 10,000 moving objects, with information of position, velocities and radar cross-section features. The approach must also take into account of geographic information such as roads, buildings, landmarks, traffic signs and signals. With above-specified inputs, the proposed method should be able to provide quantitative measures (based on a knowledge-base) of the"normalcy"of all the events happening in real-time and provide the analyst a prioritized list of areas-of-interest. For example the"normalcy"of an event can be defined as the probabilistic measure of the existence of an object at a certain location, and/or its moving behavior (across consecutive frames), and/or coexistence of multiple objects, and/or relative behavior of multiple objects. Technologies of interest may include knowledge base format and storage, supervised and unsupervised learning, knowledge base retrieval, and quantitative representation of objects behaviors over time. PHASE II: To develop and demonstrate a working prototype of an autonomous sensing, information processing and deciding framework that can run in real time using real time data supplied by the government. PHASE III: DUAL USE COMMERCIALIZATION: These techniques can be used in automated inspection or machine vision, e.g., detecting manufacturing defects, or video surveillance and also to monitor any sort of repeat remote sensing imagery (e.g., LANDSAT, IKONOS, etc.) to detect development changes for map updates.

OBJECTIVE: Develop mathematical foundations, algorithms and software to meaningfully combine heterogeneous stochastic information to deduce a realistic, accurate and mathematically-provable convergent model that is to create a mental picture of what is happening and likely to happen in the battle space environment. DESCRIPTION: Nearly all national defense missions involve Decision Support Systemssystems that aim to decrease the cycle time from the gathering of data to some operational decision. Proliferation of sensors and large data sets are overwhelming analysts, as they lack the tools to efficiently process, store, analyze, and retrieve vast amounts of data. This SBIR topic area, Data-to-Decisions, seeks to develop an open-source architecture system that enables rapid integration of existing and future data exploitation tools to achieve a new paradigm in the management and analysis of data. The initiative is pursing technologies to aid in the development of analytic approaches and advanced user-interface techniques to result in libraries of analytic and user-interaction modules that can be repurposed across a number of joint missions. Data-to-Decisions will use a proven"build-test-build"process that improves technical components by providing real-world data sets to researchers with oversight from front-line operators. Promising research avenues include: enhanced images temporal, and text analytics better software architectures improved algorithms for data fusion improved understanding of user interactions These efforts will reduce latency with higher probability of detections and fewer false alarm rates; increase situational understanding in operational missions and thus support more relevant and informed decisions; and enhance ability to navigate and find important relationships and targets in extremely large data sets. The major emphasis of this specific SBIR topic deals with the fourth bullet improved algorithms for data fusion. This proposed work should advance theoretical foundations, models, and algorithms to support timely, robust, near-optimal decision making in highly complex, dynamic systems, operating in uncertain, resource-constrained environments with incomplete information against a competent thinking adversary. The evolving field of decision sciences involves numerous research disciplines: mathematics, operations research, statistics, industrial engineering, social, economic, psychological and cognitive sciences and others. Most importantly, these disciplines to fully support operational decision making must integrate and unify research effort. Currently, these research results are not sufficiently robust, nor adequately integrated to meet the needs in making complex decisions in an operational environment. This research initiative will focus on integration of heterogeneous information to improve situational awareness and decision making, taking into account associated risks. This research emphasizes the dynamical, stochastic nature of information flow and the interaction between information processing and the commander"s cognitive and decision making process. Integrated robust, responsive, dynamical system models of information across multiple operational networks (both human and automated) must be developed to appropriately utilize all information. PHASE I: In phase I, a complete description of a robust decision support tool will be developed, including development of scientific underpinnings to adequately model and fuse complex information into a unified whole. Inference models should be developed describing sensor information flow, both physics-based sensors and human systems based data. Model should address characterizing temporal aspects of data, including non-data characteristics, historical-based patterns. Representative scenarios of realistic command-level operations and message traffic to reveal dimensions and factor structures should be developed, based on cognitive task analysis, which conceptualizes tasks of the mission space, the interrelationship of information in time and space and how these tasks will be cyclically impacted by his application of assets in this space. Advanced numerical inference modeling and optimization (stochastic) techniques should be developed. These techniques should be capable of utilizing all information by appropriately integrating this information into mathematically-based inference models. Risk must be addressed, specifically involving alternative planning scenarios. Conditional value at risk, a stochastic measure of risk, has proven to be a mathematically sound principle which has been applied in the investment community. This methodology should be applied to military decision making. Demonstration software will be produced in Phase I for both the inference models, numerical optimization, as described above. PHASE II: In phase II, developments in phase I will be extended to add more capabilities. Adding social factors to the mix of other factors is very critical. Social/cultural properties of information content/ flow should be identified and incorporated to account for the effects of these properties in processing data to account for accuracy, temporal aspects, and completeness. Biases and skewed properties should be accurately modeled using alternative distributions. Latency must be taken into account in how cultural/social properties affect timing. These dimensions will be added to the developments of phase I, including incorporation into the demonstration software. The demonstration software should be extended and integrated into a functioning test bed, where tests are conducted using realistic scenarios. From multiple tests, summary information on the advantages, qualitative and quantitative, of using an information network-based flow approach to model and build an effective, efficient tool for command-level decision support should be provided. Evaluation should be based on realistic scenarios that include variable events and that also include at least a slice of the continuum of the tactical network. Recommendations for incorporation of decision support tools, alternative organizational design, deployment of specific staff resources, and other potential improvements. PHASE III DUAL-USE COMMERCIALIZATION: Ownership of intellectual property is not intended as part of this SBIR. Rather, the more innovative approach of open-source architecture will better serve small business entrepreneurs. The output of these efforts in phase III will yield components which are easily assembled and configurable for operational users to customize and adapt to rapidly changing scenarios. As a result, innovation will continue and evolve, producing small business opportunities over the next decade.

OBJECTIVE: Design and implement a cloud enabled system that allows sensors to be dynamically tasked based on the latest status of information requirements and on-line analytic predictive processing. The system would enable an automated sensor planner to know at all times what to look for and where to look. DESCRIPTION: Compounding the large data problem faced by DoD are poorly informed sensors that can continue to add large streams of data to a data warehouse that is neither responsive to an information need or incorrectly placed relative to a collection opportunity. Sensor planning is often done well in advance of collection via many sensor or sensor type independent planning processes. A good planner may align collection with information requirements when the plan is authored, but maintaining alighment with changing mission requirmenets and/or knowledge levels is not feasible today. The use of advanced predictive tools is one way to more efficiently and effectively translate information requirements into detailed collection plans but today predictive tools often prefer to operate off-line in a batch mode. Mining a data cloud for sensor reported data could be an effective way to prevent one sensor from trying to gather information that has already become known or is no longer valued provided this can be accomplished on diverse and distributed data in real time. Similarly widgets could be used to track all published information requirements across a distributed battlespace. The direction of the services is to utilize cloud architectures to store what is known and these same architectures can be used to store what is needed to be known. Predictive analysis engines, running as real time map reduce jobs, could be used to inform sensors where to look and discovered information and discovered information needs could be used to further optimize their activity. The goal of this topic is to enable sensors to stream information vice raw data into the cloud through intelligent sensing. An initial product can consider a handful of information needs, a modest sized data cloud and a handful of distributed processors each supporting a handful of sensors. IMINT and unstructured reporting should be among the sensor considered. The matured system must be able to produce sensor tasking in real time to hundreds of sensors, informed by what needs to be known, what has become known and how collection can be optimized to meet an information need. For the overall system, the Hadoop framework should be considered. The specific challenges of this topic include: 1) Maturing predictive algorithms that can run model updates in real time as a distributed application using data discovered from a data cloud 2) Maturing a set of data miners that can discover information needs and route them to the processor that is responsible to tasking the most relevant sensor 3) Maturing a collection of widgets that constantly look inside a data cloud to see if what was needed to be known is now known or if new information requirements have been published. 4) Development of a real time sensor manager that tasks sensors based on accurate knowledge of the highest unfilled information requirements in the most efficient and effective manner possible. Research in the areas of mathematics, computer science, information theory, cloud computing, control theory, network theory and distributed sensor resource management as well as multidisciplinary areas that may prove promising are of interest. In addition to the application of research methods and approaches, it is important to evaluate the impact of these efforts areas with regards to the way they change how tasking is designed and data is collected to positively impact decisions. The OSD is interested in innovative R & D that involves technical risk. Proposed work should have technical and scientific merit. Creative solutions are encouraged. PHASE I: Complete a plan and detailed approach for populating an architecture that supports in real time the information needs of a sensor manager. Retire risk associated with on-line predictive analysis and real-time discovery of targeted information in a cloud. Identify the critical technology issues that must be overcome to achieve success. Technical work should focus on the reduction of the identified key risk areas to a phase II prototype. For a bounded set of information needs and sensors populated within a modest sized cloud architecture, demonstrate that phase 1 risk reduction work has shown that a full implementation of the approach is technically tractable. Prepare a revised research plan for Phase 2 that addresses critical issues. PHASE II: Produce a prototype system that is capable of distributed sensor management in a cloud environment. The prototype system should continuously assess what needs to be known, what is already known and what predictive models are saying is the best way to collect the balance. The system should run across a distributed architecture, enabled by map reduce jobs and ozone widgets, The system should provide in real time a computed sensor field efficiency index, a measure of how well current sensor tasking is expected to be against unknown information needs. The prototype should enable a demonstration of the capability to be conducted using relevant sensor models, data streams and information requirements, some of which may be classified. The prototype should be capable of operating in a real time mode. The prototype should be relevant to both DoD and commercial homeland defense and security use cases. PHASE III DUAL-USE COMMERCIALIZATION: Produce a system capable of deployment in an operational setting. The work should focus on a specific user environment intended for product transition. Test the system in an operational setting as a component within a service cloud architecture. The performer should work towards a transition to program of record, a military organization and a commercial product. The system should adhere to open standards and be delivered with full Government purpose rights.

OBJECTIVE: Establish intuitive decision-centric approaches to simplify complex information fusion (IF) techniques for rapid collection, processing, and understanding of large stores of data. DESCRIPTION: The Data to Decisions (D2D) initiative, noted as one of the top seven Science and Technology challenges for the US Department of Defense, has been largely defined and implemented as an information fusion (IF) problem. As [1] notes, this community grew out of the sensor fusion effort that has fusion techniques that don"t scale with increasing data sizes. As the D2D program elements have developed to address the identified challenges of data size, processing, and decision support, the decision-theoretic foundation of IF has not changed. IF systems continue to presume a rational decision model for users, when cognitive and social scientists have shown the predominance of models such as naturalistic or intuitive decision making, sensemaking, etc. Researchers are exploring the cognitive connection in IF. The Cognitive-Observe-Orient-Decide-Act (C-OODA) loop is discussed by [2], and [3] suggest that cognitive methodologies are needed to understand how individuals use IF systems and their respective decision making processes. This thinking is further extended by [4] to explore how an IF system would be designed if it were based on a sensemaking (rather than a rational) model of decision making. The challenges recognized by D2D essentially capture the reality that IF systems are not able to handle the current challenges facing human decision makers. Two related developments have occurred within the past decade of asymmetric warfare. Central among these is the dynamic nature of asymmetric warfare, for which researchers have devoted great attention. The Counter-Insurgency (COIN) doctrine [5] and the DOD Human Social Cultural Behavioral (HSCB) Modeling Program [6] have identified the non-lethal and non-kinetic factors that must be addressed for current and future military operations. On a parallel path, the advances in information technology and the growth of social media applications on the global internet have added to the ways in which people share information and exert influence for personal and business purposes. The Arab Spring demonstrations were a lesson in how open source technology can be used for political and social purposes [7]. These demonstrations may also have created a paradoxical situation that could signal a new era in IF for decision support. If the online and open source media provides a relevant source of data that includes some meaningful meta-data, perhaps a new variety of IF system can be imagined; and one that provides functional activities that can overcome the biases present in each human decision maker. Examples of biases include values and beliefs, world reference models, preconceptions and objectives, previous experience, and cultural traditions [4]. Addressing confirmation bias and aligning with cognitive strengths of individuals are additional goals. We seek novel exploitation of intuitive decision-theoretic concepts as they can be applied to the development of an information fusion system that can provide information in a way consistent with the underlying construct of how people perceive and react to information in time-constrained and complex situations. Challenges in this topic include selection of a decision foundation, designing an approach to information integration and presentation, and designing a concept for visualization of information that is consistent with the underlying theory of how people perceive and use information in cognitively constrained situations. We seek innovative and novel approaches to what has become in the military a highly engineered system that does not mimic the decision processes followed by human users. We are interested in defining critical points in a decision process that can be re-engineered with respect to IF to improve the decision maker"s understanding of the environment. Challenges in the fusion process should include new approaches to dealing with uncertainty and unknown factors in the environment and hypothesis testing. Throughout the development process, performance measures should be developed. PHASE I: Conceptualize and define an innovative approach to information fusion that is structured upon an intuitive theory of decision making. Identify and define the theoretical approach and provide examples of how a fusion architecture would be composed to collect, integrate, and present information in ways that are consistent with the theoretical approach to human understanding of this information. At the conclusion of Phase I, produce a conceptual design and breadboard of a small fusion architecture that clearly reflects novel instantiations of the supporting decision theory. Phase II plans should also be provided, to include key component technological milestones and plans for testing and validation. PHASE II: Develop, demonstrate, and validate a prototype system based on the preliminary design from Phase I. All appropriate engineering testing will be performed, and a critical design review will be performed to finalize the design. Phase II deliverables will include a working prototype of the system, specification for its development, and a demonstration of the decision-centric fusion tool. PHASE III: This technology will have broad application in military, government, and commercial settings. Within the military and government, there is an increasing emphasis on information fusion technologies that aid decision makers responding to foreign nations that are potentially hostile to the US and Coalition interests. Developing models and tools that can rapidly integrate and present information in ways that compliment a user"s decision making process will be a powerful addition to strategic, operational, and tactical decision making. The proposed effort will enable the delivery of more informed courses of action supported by tractable information sources.

OBJECTIVE: Research and develop automated capabilities to extract events with modality, polarity, genericity, and tense attributes from unstructured textual data sources, such as news articles or Human Intelligence (HUMINT) reports, to enable persistent Situational Awareness. DESCRIPTION: Intelligence analysts need the ability to rapidly monitor and analyze event information in large volumes of unstructured textual data, such as news articles or HUMINT reports, in order to achieve and maintain persistent Situational Awareness (SA). For example, the ability to stay apprised of events that have already occurred, as well as events that are threatened or planned, would be a valuable contribution to an analyst"s SA. The problem is that the amount of unstructured textual data available is well beyond what can be manually read and processed in the time available. A capability enabling analysts to rapidly extract event information from large volumes of unstructured text and store it in a structured form, such as a database, is needed to improve an analysts"ability to maintain persistent SA. The goal of this topic is to advance the state-of-the art for extracting events with their attributes of modality, polarity, genericity, and tense from large volumes of unstructured text. Modality of an event indicates if the event was a real occurrence. Examples of event modality include asserted, i.e."The bomb exploded on Sunday;"believed, i.e."It is rumored he will be sentenced;"hypothetical, i.e."If he were arrested, he would be convicted of murder;"and threatened, i.e."He threatened to attack the country."Event polarity indicates whether the event actually occurred. For example,"The city was not attacked"is an event with negative polarity, and"The attack occurred on Sunday"is an event with positive polarity. Genericity indicates whether an event is specific, i.e."The city was attacked on Saturday", or generic, i.e."They specialize in transporting weapons."Tense indicates whether an event occurred in the past, is occurring in the present, or will occur in the future. Secondary challenges include, but may not be limited to, rapid customization to different sources/styles/formats of textual data, and rapid customization to various domains (areas of interest). While addressing other technology gaps that would contribute to the capability would be useful, it is optional since it should not happen at the expense of addressing the primary research challenge of extracting event attributes. Capturing events with attribute information and storing them in a structured form would enable more efficient exploitation of event information, as well as the use of automated analysis and visualization tools. Most event extraction systems extract event mentions and arguments, but do not extract the modality, polarity, genericity, and tense attributes of the event. Automatically extracting event attribute information and storing it in a structured form would enable more efficient search techniques, such as allowing an analyst to search for threatened or planned events, and support rapidly identifying relevant information. This capability would also enable analysts to leverage information from large collections of textual documents, and as well as save the information for use by other analysts and use in the future, which would support establishing and maintaining SA. PHASE I: Research and develop techniques for extracting events with modality, polarity, genericity, and tense attributes. Experiment with, and assess the feasibility of different approaches, with the end goal of achieving high accuracy extraction. Based on these results, develop an initial design for a prototype to extract events with attributes from unstructured text. Finally, develop a feasibility demonstration that substantiates the design and chosen approach. PHASE II: Perform in-depth research and develop a full scale prototype to extract events and attributes from large volumes of unstructured text. The government"s intent is to achieve an improvement in the state-of-the art and to identify a user with real data to support this work. Demonstrate the effectiveness of the capability for supporting persistent Situational Awareness by addressing a comprehensive set of event types and including the attributes of modality, polarity, genericity, and tense. Evaluate and substantiate the performance of the prototype by obtaining measures of precision, recall and F-measure against a test data set. PHASE III DUAL-USE COMMERCIALIZATION: The research and development of this technology will improve the state-of-the-art of event-attribute extraction. The research has a wide range of potential applications within government and commercial markets. Essentially, any application that deals with information overload and the monitoring of events can be assisted by the developed capability. Military Application: Intelligence analysts would benefit from a capability enabling rapid discovery of event information relevant to their analysis tasks within large volumes of text. Commercial Application: Law Enforcement, Homeland Security, financial and medical markets could use this capability to rapidly identify events of interest, such as planned or threatened events. This phase will explore, pursue and market paths for military and commercial applications of the developed technology. This phase will also focus on inserting and evaluating the performance of the developed technology in operational environments.

OBJECTIVE: Design and implement system that combines audio transcription and translation tools with natural language processing capabilities that enable actionable information to be automatically extracted from audio files. DESCRIPTION: Text analytics is a growing field and central to the war on insurgents. While advances have been made in the development of tools that can extract entities, associations, concepts (word frames) and themes from English written text documents, additional work is required to show that actionable intelligence can be automatically extracted from foreign language audio. When transcription and translation tools are placed in front of natural language analytics the task of automated understanding is challenged by word error rates. Given a goal of an acceptable level of machine characterization of the content of an audio file, a future system must track error rates after every step (translation, transcription, natural language processing including entity and association extraction as well as concept and theme recognition) , make corrections and track confidence levels. Systematic errors can be corrected for using replacement lookup tables. Random errors may be corrected through some sort of word voting scheme. For this topic, offerors may work with audio files spoken in one foreign language. Phase 1 performers may work with open source data. The specific challenges of this topic include: 1) Improving the key word error rates of transcription services 2) Improving the key word error rates of translation services 3) Improving the robustness to word error rates to entity and association extraction 4) Improving the uncertainty calculation associated with the output of a natural language engine that starts with audio 5) Demonstrating concept and theme identification can be robust to word error rates. Research in the areas of linguistics, natural language processing, mathematics, statistics, computational data analysis and visualization, computational sciences and computer science are of interest. In addition to the application of research methods and approaches, it is important to evaluate the impact of these efforts areas with regards to the way they change how data is collected, analyzed and assessed to meet a prescribed time for operational necessity and efficiency. It is of value to use open standards to reduce costs. The OSD is interested in innovative R & D that involves technical risk. Proposed work should have technical and scientific merit. Creative solutions are encouraged. PHASE I: Complete a plan and detailed approach for developing an automated system for characterizing the information content of a foreign language audio file. Identify the critical technology issues that must be overcome to achieve success. Technical work should focus on the reduction of key risk areas. For a constrained set of languages (one is acceptable) demonstrate that phase 1 risk reduction work has shown that a full implementation of the approach is technically tractable. The matured system should show that natural language applications such as entity and association extraction, concept (word frame) identification and theme recognition can follow transcription and translation applications. Prepare a revised research plan for Phase 2 that addresses critical issues. PHASE II: Produce a prototype system that is capable of entity and association extraction, concept (word frame) identification and theme recognition at acceptable error rates when processing audio files. Produce a prototype processing service that can operate in a cloud architecture to enable a high throughput of data. The prototype should enable a demonstration of the capability to be conducted using relevant data sources, some of which may be classified. The prototype should be capable of operating in a real time mode. Identify appropriate test performance dependent variables and make trade-off studies. The prototype should be relevant to both DoD and commercial use cases. PHASE III DUAL-USE COMMERCIALIZATION: Produce a system capable of deployment in an operational setting. The work should focus on a specific user environment intended for product transition. Test the system in an operational setting in a stand-alone mode or as a map reduce job in a cloud architecture. The work should work towards a transition to program of record, military organization or commercial product. The system should adhere to open standards and open source software wherever feasible.

OBJECTIVE: Distributed warfighters operating small handheld tactical communications and computing devices are experiencing increased workloads, decreasing the potential benefit of using those devices. The operator needs to receive relevant information tailored to the specific mission requirements with minimal input to the device. The objective is to develop automated tools that can help determine the value of the information to the user and send only needed information when it is needed. DESCRIPTION: The amount of data streaming into tactical systems is overwhelming the operators trying to make sense of unfolding situations. There are often multiple sources reporting on the same event that is of critical interest to the operator. The operators need ready access to the most pertinent and valuable information for their given situation. Current systems do not assign relative values to the incoming data so that the systems can prioritize or highlight the presentation of the information to the operator. It may even be the case that the highest value information is the result of a combination of two or more reports that compliment and/or confirm an event. ONR is seeking a framework for managing information with emphasis on the relative value of that information for a wide range of operations. The anticipated employment of this framework would be within a combat operations center with secure connectivity to higher and adjacent commands. Additionally, this framework needs to be extensible to support distributed small units operating over intermittent, low throughput tactical wireless networks. Solutions that can automate common tasks and information requirements for the small unit leader, and be delivered over tactical communications in a mobile ad-hoc networking (MANET) environment. Potential solutions will be used in foot mobile operations and will need to work within a minimized size, weight and power (SWAP) environment. PHASE I: Determine methodology for determining information value. Perform simulation and modeling to show how the proposed methodology will effectively increase the quality and usefulness of information provided. Develop plan for implementing the methodology in a practical system. PHASE II: Develop and demonstrate the selected methodologies with contractor-supplied data sources. Further develop a decision support capability that can be further demonstrated to provide 90% correct information, within a useful time period, with only 10% of needed information missing compared to data processed by human experts. PHASE III: DUAL USE COMMERCIALIZATION: Provide a TRL 7 decision support system integrated into a selected program of record. Demonstrate system on Government-provided data with the same correctness as shown in Phase II.

OBJECTIVE: Apply semantic targeting to large and complex, uncertain, contradictory and incomplete data sets to extract decision relevant intelligence information based on filtering by context and meaning. DESCRIPTION: The 2012 National Security Strategy has indicated that"for the foreseeable future, the United States will continue to take an active approach to countering [threats] by monitoring the activities of non-state threats worldwide". Groups and organizations are frequently informally structured, and operate as distributed entities on an ad hoc basis. Further, with ever increasing decentralization of decision-making enabled through ubiquitous electronic communication, there is a need to identify threats in large and complex, uncertain, contradictory and incomplete data sets available in the open source environment. The resulting flood of multi-sensor, multi-source data overwhelms analysts and operators because there is too much data. Information needs to be extracted based on its likely value for planning and intelligence needs. Semantic targeting may provide a vehicle to mathematically extract data more likely to be of value as information. Semantic targeting has been used in online advertising to identify high-value targets for product marketing programs. The approach leverages principles of the semantic web combined with behavioral targeting and contextual advertising. A similar approach could be applied to open source intelligence analysis where the challenge is dealing with deriving and/or detecting information intent and contextual uncertainty from large quantities of communication data. The optimal data set might be one day of data collected at a Major Military Command for use in the Commander"s daily status brief. Unclassified data collected could include: multinational and NGO email, local news media publications and broadcasts, social media traffic and blogs, government publications and statements and general Internet traffic. Other available data sets of a similar nature would be public and government communication during Katrina or public and government communication during the events surrounding 9/11/2001. The employed search techniques would be modulated and informed by cognitive science-based algorithms that would prioritize, filter, fuse, interpret, and graphically display results for intelligence analyst review. Models of analyst-based behavioral inferencing, and trust would be incorporated into the algorithms. A possible approach would use two types of sorting and matching mechanisms: 1) Mathematically model analyst behavior in terms of how that analyst extracts intent and meaning from a corpus of ill-structured open source data; 2) Use semantic analysis of the data corpus to sort the data and to extract intent and meaning based on modeled analyst behaviors. PHASE I: Proposals for Phase I will incorporate a literature search of semantic targeting research and applications in order to suggest a baseline for design and construction of a conceptual model. The proposed conceptual model will incorporate search techniques and supporting algorithms in a design that could be tested and validated with a corpus of data such as that generated during the Katrina or 9/11 incidents. Phase 1 must identify a proposed operational context, i.e., application, for developing and demonstrating the proposed model and identify data that would be used in validating the algorithms. PHASE II: Develop and demonstrate a prototype instantiation of the conceptual model. Conduct test bed-based validation in an experimental or simulated environment. Initial demonstrations may be conducted using a notional scenario and synthetic data. However, evaluations with actual data must be conducted before the end of phase 2. Software documentation and guidelines for application must be included in the phase 2 report. A controlled yet realistic experiment must be conducted to quantify the anticipated improvement in intelligence analyst performance using any algorithms developed. PHASE III DUAL-USE COMMERCIALIZATION: The developed prototype could be used in other military initiatives (concept engineering) or could be applied to business models (web-based or market-based) that require an assessment of product acceptance by the public or a selected corporate market area.