DoD 2018.C STTR Solicitation

TECHNOLOGY AREA(S): Human Systems 

OBJECTIVE: To biologically define the human threat response in a context that may explain stress related attrition during training and a degraded awareness of discipline on the battlefield for SOF. 

DESCRIPTION: Hundreds of Airmen train each year in the Battlefield Airmen training pipeline where a large attrition rate occurs leading to millions of DoD dollars spent on Airmen that will not make it to SOF. Though some of these individuals leave due to adverse medical events or a lack for performance competency, a large number of trainees leave by self-initiated elimination (SIE). Anecdotal discussions often reveal these uncontrollable, immediate decisions are followed up by a conscious decision to NOT remove themselves from the course. This overwhelming surge of stress cannot be sensed and assessed through interview or questionnaire methods, particularly if the SIE decision may have been unconsciously made. At the operational level, with possible increasing duration of hostilities, one may lose the ability to maintain awareness of discipline on the battlefield. This loss of discipline may lead to the inability to conduct the mission under the rules of engagement. A certain combination of biomarkers have been shown to differentiate those who generally express resilient behavior under stress (e.g. Neuropeptide-Y and DHEAS/Cortisol ratios). However, in order to capture "in the moment" spikes in the stress response, there is a need for a neurochemical-based biosensor that will sense the regulation of the neurotransmitter system during highly threatening events. The Norepinephrine/Epinephrine (N/E) system represents regulatory cognitive and emotional processes that are manifested with changes in human behavior such as alertness, decision making, and anxiety. NPY serves to aid the individual in maintaining an ideal level of arousal in response to a stressor without being overwhelmed (McNeil & Morgan III, 2010). Kourtesis, Kasparov, Verkade, and Teschemacher (2015) documented that it is well established that NPY can be co-released with N/E in the periphery (Lundberg et al., 1986; Pernow, 1988; De Potter et al., 1997; Johnson, 2010). A real-time sensor that will capture the cotransmission of these neurochemicals could reveal an objective and definable explanation for the overwhelming and uncontrollable stress response, such as SIE events or suddenly being overcome by events on the battlefield. Special operations training facilities may be accessed under this work. In addition, AF operators may also be requested to participate in research. 

PHASE I: Phase I involves a detailed analysis of biochemical processes involved in the human threat response under high stress as well as an assessment of the current state-of-the-art in biochemical sensor development. In addition, the platform for sensor development should be identified considering analyte detection, biofluid mechanism (blood and saliva are ideal), and range concentration. Phase I will determine the need for the sensor as well as a "plan" for development to be implemented in Phase II. 

PHASE II: Phase II will involve the development of a biosensor that can rapidly detect biomarkers of interest (e.g., Neuropeptide-Y and Norepinephrine) within a 10 minute period in a high stress environment. Phase II will involve development as well as sensor verification (does the sensor accurately assess the specified markers?). Assessment constraints need to be considered such as collection timing and environmental feasibility. It is important to note that feasibility testing may take place in a field environment, while the verification and validation testing may occur in a controlled laboratory setting. Year I of Phase II should be dedicated to design/redesign efforts with feasibility and utility in mind, while Year II of this phase should include a testing period validating the sensor as well as correlating cognitive changes that may result from optimal/suboptimal biomarker levels. Sensor development verification can be accomplished by the collection of biofluids in concert with the sensor, or previously collected (banked) samples that were collected during post high stress events. Feasibility testing will require customer support and access to an environment of potential transition (e.g., location of water confidence training). The ability to make decisions following these events should be assessed through standard cognitive testing applications. Finally, a Phase III plan should be in place following the finalized reporting of Phase II. 

PHASE III: If successful, this SBIR/STTR can be transitioned through the Human System Integration office of the 711th Human Performance Wing. The Phase II product may be applicable in many commercial environments where sudden exposure to stress may be encountered, such as first responders and emergency room medics. Commercial organizations should be contacted for appropriate application and distribution. Phase III is specific to commercial/military application for high stress events that require cognitive precision. 

REFERENCES: 

1: Johnson, C. D. (2010). A demonstration of sympathetic cotransmission. Advanced Physiological Education, 34, 217-221.

2:  Kourtesis, I., Kasparov, S., Verkade, P., & Teschemacher, A. G. (2015). Ultrastructural Correlates of Enhanced Norepinephrine and Neuropeptide Y Cotransmission in the Spontaneously Hypertensive Rat Brain. ASN NEURO, 7(5), 1-19.

3:  McNeil, J. A., Morgan, C. A. (2010). Cognition and decision making in extreme environments. In C. H. Kennedy & J. L. Moore (Eds.). Military Neuropsychology (pp. 361-382). New York, NY: Springer.

KEYWORDS: Biomarkers, High Stress, Catecholamines, Decision Making, Special Operations, Attrition, Neuropeptide-Y, Biosensing 

CONTACT(S): 

Regina M. Shia 

(937) 938-3570 

regina.shia@us.af.mil 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Research, design and develop an information management capability incorporating a multilevel security methodology, framework and techniques to securely store, synthesize, and disseminate cyber information related to military systems. 

DESCRIPTION: The DoD has an urgent need to develop a multilevel security (MLS) information management capability capable of collecting, sharing and disseminating cyber information containing threats, system vulnerabilities, mission impacts, and risks. Secure collection, storage, access, and dissemination must be ensured while confirming users’ need-to-know and clearance credentials. Designing, developing, and demonstrating a cyber information management system to provide secure information sharing tools for warfighting, acquisition, and command organizations is critical to developing weapons system protection and mitigation technologies. Due to the sensitive nature of cyber vulnerabilities and mitigations of DoD platforms and especially multiple platforms, any database developed must provide multi-level security. Based on queries of the management system, the tool must automatically search for other platforms that have experienced similar problems (at any security level) and their solution (if one exists) without compromising security. To date, no commercial MLS products or known MLS techniques can be used for these purposes when applied to cyber information about military systems. Offerors are sought to propose and innovative concepts that provide an architecture to collect, secure and disseminate cyber information that implement the appropriate security classification policies for defining rules and queries, verifying clearances, and providing proper workflow approvals. The tool should enable real time availability of cyber information to appropriately cleared parties with a time-sensitive information needs. The MLS database should have the capability to be mirrored on multiple networks and able to transfer information from lower classification networks to higher classification networks easily with proper safeguards. The metrics for evaluating proposed MLS techniques/framework are confidentiality, integrity and availability properties of cyber information from sender(s) to receiver(s). The goal is 100% for each. The offeror must develop and propose a plan on their approach to achieve the goal for each metric. 

PHASE I: Design, document and demonstrate a feasibility proof-of-concept of the proposed MLS framework and tool. One approach is to use a conceptual ‘walk-through’ or mock-design. For example, 1) develop and document detailed technical requirements for meeting cyber repository MLS needs, 2) perform survey of internal capability and market research, 3) develop Concept of Operation and appropriate architectural documentation such as DoDAF artifacts (OV-1/AV-1/SV-1), and 4) establish strategy to secure data at rest and transit as well as way to confirm appropriate approvals. 

PHASE II: Based on the result from Phase I, refine and extend the prototype system design to a tool or framework that could collect, share, and disseminate to warfighters securely with actual data sets. Demonstrate the capability, effectiveness and usability of the framework and tool. 

PHASE III: The proposed MLS framework and tool should be enhanced to store, share, integrate, and disseminate cyber information/data to warfighters securely with need to know basis for military and commercial applications. 

REFERENCES: 

1: "Components and Technologies". Adobe Digital Enterprise Platform, 09 Feb.2018, help.adobe.com/en_US/enterpriseplatform/10.0/OverviewADEP/WSfc76cf3007e1a2a352792fba131956652ae-8000.html

2:  "Multilevel Security." Wikipedia: The Free Encyclopedia. Wikimedia Foundation, Inc. 08 Feb. 2018 Web. 09 Feb. 2018, en.wikipedia.org/wiki/Multilevel_security.

3:  "Defense Information System Network." Wikipedia: The Free Encyclopedia. Wikimedia Foundation, Inc. 08 Feb. 2018 Web. 09Feb. 2018, en.wikipedia.org/wiki/Multilevel_security.

4:  National Vulnerability Database – Homehttps://nvd.nist.gov

KEYWORDS: Classified Repository, Data Analytics, Data Workflow Approval Process 

CONTACT(S): 

Jong Hwang 

(937) 713-8024 

jong.hwang@us.af.mil 

TECHNOLOGY AREA(S): Sensors 

OBJECTIVE: The objective is to develop a Sample Clock Modulation technique for a novel prototype receiver architecture capable of up to 30 GHz of instantaneous bandwidth (IBW) with dramatically reduced size, weight, heat dissipation, and power consumption when compared to traditional receiver architectures. 

DESCRIPTION: Existing front end receivers for EW applications are typically Superheterodyne Tuning Receivers (STR) with an IBW from a few megahertz up to a maximum of 4 GHz. This type of receiver produces slow detection times, especially for low duty cycle signals, and as such, will miss modern, dynamic, agile LPI/LPD waveforms. Detection time issues will continue to worsen as the RF spectrum of interest grows into the Ka, Q, and V bands (27 to 75 GHz). An alternative receiver architecture, called the ultra wideband receiver (UWR), would provide dramatic improvements over the STR architecture in many ways, including detection time, ability to see every pulse throughout the RF spectrum, cost to build, size, and heat dissipation. The UWR architecture uses direct digitization in conjunction with undersampling signal compression techniques to continuously “stare” at the entire RF spectrum to greatly enhance the IBW stare coverage. While recovering Nyquist zone information in the folding process is a challenge, the first-level situational awareness gained with this approach can guide narrowband processing for higher fidelity captures. Using the UWR approach, it is feasible to build a single front end receiver capable of detecting, capturing, and analyzing every pulse within an IBW of 20 to 30 GHz without dropping pulses. The UWR architecture also benefits from simpler, lower cost hardware by eliminating costly RF components and instead converting analog directly into the digital domain. STR receivers require multiple local oscillators, mixers, demodulators, bandpass filters, fast switches, amplifiers, and attenuators that are not necessary in the UWR architecture. The UWR architecture offers advantages that could provide dramatic impact and benefit by delivering comprehensive spectrum situational awareness. A UWR receiver would have the ability to detect and track multiple, agile, highly dynamic sets of LPI/LPD targets. Targets that are very frequency agile can easily move outside the bounds of an STR receiver, but the UWR architecture is capable of tracking every pulse within a 20-30 GHz RF spectrum. This wideband collection capability allows UWR to collect data against targets without prior knowledge about target characteristics or location. Receiver applications, such as SIGINT/ELINT data collection system, radar warning receiver (RWR), low probability of intercept (LPI) receiver, and RF test equipment, can all benefit from advantages associated with the UWR architecture. Additionally, the low-cost UWR architecture will enable wide deploy of these receivers with more capability, a smaller footprint, reduced power requirements, and improved reliability. UWR requires substantially fewer components, less power, and relies primarily on digital components with better overall reliability when compared to the many active analog components in alternative receiver architectures. This topic is seeking innovative techniques and design methodologies to support the warfighter and the technology insertion portfolio of AFSC. Factors that need to be considered when choosing the approach are frequency determination, dynamic range, and sensitivity and many others. An XRT framework should integrate understanding and modeling of: 1 - the different manufacturing technologies used to manufacture PWBs; 2 - the materials, processing, and defect types in electronic components; 3 - the development, progression and criticality of damage in PWBs and electronic parts; 4- the interaction between the different damage types and the interrogation method used to monitor part for integrity (such as ultrasonic, electromagnetic, thermal, visual, etc.); 5 - as well as the reliability of the approach itself. 

PHASE I: Investigate and model a novel sample clock modulation approach designed to address the frequency determination challenge that encodes modulation on signals that can be used to recover Nyquist zone information that is lost in the “folding” process 

PHASE II: The Phase I model will be optimized and expanded to address sensitivity through the use of digital filtering to reduce thermal noise, and addressing dynamic range through digital filtering and proper hardware selection to maximize the receiver’s effective number of bits. 

PHASE III: If Phase II is successful, the company will be expected to support the Air Force in transitioning the technology for use. Working with the Air Force, the company will integrate the technology for evaluation to determine its effectiveness in an operationally relevant environment. 

REFERENCES: 

1: J. D. Taylor, Ultra-Wideband Radar Technology. New York: CRC Press, 2001.

2:  J. D. Choi and W. E. Stark, "Performance of ultra-wideband communications with suboptimal receivers in multipath channels," IEEE J. Selected Areas Comm., vol. 20, pp. 1754–1766, Dec. 2002.

3:  H. Ishida and K. Araki, "A design of tunable UWB filters," in Proc. Int. Workshop Ultra Wideband Systems, 2004, pp. 424–428.

KEYWORDS: Electronic Warfare, Ultra Wideband Receiver 

CONTACT(S): 

Troy Anderson 

(478) 327-9156 

troy.anderson.12@us.af.mil 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop innovative methods to present, manipulate, and analyze large multi-dimensional data sets to revolutionize the systems analysis of modeling & simulation and test data. 

DESCRIPTION: This topic seeks novel and cutting-edge methods, techniques and tools in the manipulation, analysis, and presentation of extremely large multi-dimensional data sets to assist analysts in detecting, understanding, and presenting complex and subtle relationships within massive multi-dimensional data sets. The government amasses extremely large quantities of test, simulation and operational data. Much of the data can be comprehended by analysts only through incidental circumstances—cases of unexpected or anomalous behavior that brings attention to specific data chains in single test/simulation runs. Potentially significant correlations and trends in the data are lost in the mass of data. From these data sets, analysts must identify trends, discontinuities, and unexpected correlations, and then develop an understanding of discovered relationships. The emphasis of this topic is on new and novel ways to visualize and present information, and should incorporate big-data analytics tools only as an enabler to accomplish these visualization advancements. This capability should be generally independent of the actual data analyzed and allow an intuitive manipulation of the data items. Potential methodologies include, but are not limited to, autogenous models and metrics, big-data analytics, and presentation psychology, multi-sensory composites. Advanced presentation techniques might include use of 3-D, motion, vibration/dithering, color, texture, sound, etc. 

PHASE I: Conduct a demonstration/proof-of-concept of the viability of the proposed approach. Deliver an algorithmic/process description of the developed approach, to include use-case descriptions and descriptions or demonstrations of output. Provide a plan for the development of an initial working prototype capability, to include cyber security efforts to gain approval to operate on government computers. 

PHASE II: Deliver an initial working prototype capability to the government for use on an experimental basis. The following are anticipated by the conclusion of Phase II: a) Demonstration of a functional initial capability b) Prototype software for experimental trials by government users c) Documentation, including software scan results, to support approval decisions to load software onto government computer systems d) Documentation of the initial capability sufficient to support trial use e) Documentation of software architecture, its algorithms/processes, and output formats f) Development plan for a full operational capability 

PHASE III: Deliver phased incremental improvements to the prototype until a full operational capability is achieved. At each increment, the following are anticipated: a) Demonstrations of incremental additions/improvements b) Software release for use/testing by the government c) Documentation, including software scan results, to support approval decisions to load software onto government computer systems d) Updated user documents e) Updated architecture, algorithms/processes, and output format documentation At the conclusion of Phase III, the final software and documentation should be in a clean usable form. 

REFERENCES: 

1: C. Ziemkiewicz, R. Kosara. "Embedding Information Visualization Within Visual Representation." Univ. of North Carolina Charlotte. http://viscenter.uncc.edu/sites/viscenter.uncc.edu/files/CVC-UNCC-10-15.PDF

2:  M. Tory, T. Moller. Jan-Feb 2004. "Human Factors in Visualization Research." IEEE Transactions on Visualization and Computer Graphics. Vol 10

3:  Issue 1

4:  J. Kehrer, H. Hauser. March 2013. "Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey." IEEE Transactions on Visualization and Computer Graphics. Vol 19

5:  Issue 3

KEYWORDS: Big Data, Visualization, Analysis, Correlation, Trends, Presentation Psychology, Autogenous Models, Autogenous Metrics 

CONTACT(S): 

Shawn Sloan 

(719) 721-4616 

Shawn.sloan@mda.mil 

James Ceney 

(719) 721-9265 

TECHNOLOGY AREA(S): Air Platform, Battlespace, Weapons 

OBJECTIVE: Develop aerodynamic controls for high G maneuvers in hypersonic environments. 

DESCRIPTION: This topic seeks concepts and studies for aerodynamic controls capable of high G maneuvers in a hypersonic environment. Concepts should focus on maximizing maneuverability and minimizing kinetic energy losses to the greatest extent possible while allowing controlled flight in a hypersonic environment. Studies may include various geometries, materials, positioning for control surfaces, or other innovative concepts. Proposers may assume vehicles are simple conical shapes or shapes with greater lift-drag ratios. Proposers may also assume a range of velocities above Mach 5 and a range of altitudes up to 50 kilometers. Solutions could have applicability to small interceptors, such as projectiles shorter than one meter or larger interceptors, such as missiles over 5 meters long. The government has invested extensively in technologies for cruciform divert systems, but those technologies are not desired for this Broad Agency Announcement 

PHASE I: Perform analysis on promising aerodynamic control concepts to include modeling and/or limited wind tunnel assessment. Estimate the maneuverability and kinetic energy loss for maneuvers at a range of Mach numbers and altitudes. Down select to one or two preferred design concepts. 

PHASE II: Work with a missile defense system integrator to mature the selected geometry and design. Obtain higher fidelity estimates of performance. Test in a representative environment such as a wind tunnel. 

PHASE III: Work with a system integrator to refine requirements and integrate into a full guidance navigation and control system. Demonstrate the technology in a representative environment. Transition the technology into a missile defense application. 

REFERENCES: 

1: Aerodynamics of Hypersonic Lifting Vehicles. November 1987. http://www.dtic.mil/dtic/tr/fulltext/u2/a198665.pdf

2:  M. Landers, L. Hall, L. Auman, and M. Vaughn. Deflectable nose and canard controls for a fin-stabilized projectile at supersonic and hypersonic speeds. (21st AIAA Applied Aerodynamics Conference)

3:  M. Matarrese, A. Messiter, T. Adamson. 1991. Control of Hypersonic Aerodynamic Forces with Surface Blowing. AIAA Journal Vol. 29

4:  Issue 12

KEYWORDS: High G, Hypersonic, Aerodynamic Control 

CONTACT(S): 

Kevin Krueger 

(256) 955-4136 

Kevin.krueger@mda.mil 

TECHNOLOGY AREA(S): Materials, Sensors, Space Platforms, Weapons, Nuclear 

OBJECTIVE: Develop new materials, or new surface processing techniques for existing materials, to use in the fabrication of absorptive baffles (visible and infrared) for exo-atmospheric sensor or seeker telescopes. 

DESCRIPTION: This topic seeks innovative new baffle materials, or new surface processing techniques for existing baffle materials, that achieve the following goals: a) A specular reflectance of less than 1% across the 0.4 um to 25 um waveband b) A total hemispherical scatter of less than 1% [same waveband] c) A Lambertian bidirectional reflectance distribution function (BRDF) of about 5E-3 [same waveband] d) Lightweight, good thermo-structural properties, low particulate generation, and ability to survive harsh natural and manmade environments Baffles reduce stray-light within optical telescopes in order to improve the sensitivity of missile-defense sensors and seekers. Baffle materials used within exo-atmospheric optical sensors must be lightweight, have good thermo-structural properties, and survive harsh environments. Some materials (e.g., beryllium, silicon, and silicon carbide) have these attributes but are generally too reflective, in the visible and infrared, to use as absorptive baffles without surface processing. Most current absorptive material surfaces cannot survive nuclear, shock, vibration, and cryogenic environments without some optical performance degradation and/or particulate generation. Ion-assisted texturing of existing materials (e.g., aluminum and beryllium) appears to be a promising surface processing technique that could meet the goals of this topic. However, the government does not endorse any particular solution and will consider alternative approaches. New absorptive materials (to include high purity elements, alloys, coated substrates, and/or laminated materials) will also be considered; provided the materials’ other properties are comparable or superior to existing materials that are used in the intended application. Proposers should detail their manufacturing methods or processes and what process control(s) are required in order to produce the desired material properties. 

PHASE I: Fabricate and test a minimum of 12 small samples (~1-3 inch dia.) of either the objective material or a suitable substitute material that adequately demonstrates the surface processing technique. Measure the samples’ specular reflectance at incident angles from 5° to 85°. Measure the samples total hemispherical scatter at an incident angle around 30° to 45°. Conduct sample screening tests to quantify the particulates generated when exposed to harsh environments. Deliver the samples to the government for independent performance verification. 

PHASE II: Measure the BRDF of objective material samples at room temperature and at angles from about 2° to 88°. Quantify particulate generation and optical degradation of material samples after exposure to shock, vibration, and cryogenic cycling. Fabricate and test a minimum of 6 baffle prototypes approximately 25 centimeters in outside diameter, with an inside diameter of about 20 centimeters, containing a tapered knife-edge with a radius of curvature of between about 0.0025 and 0.025 centimeters. Repeat optical characterization and environments testing on the prototypes. Deliver the prototypes to the government for independent optical, thermal, structural, and nuclear environment performance testing. 

PHASE III: Work with a prime contractor/integrator to design, fabricate, and integrate a baffle system for a prototype optical sensor or seeker and assist with sensor or seeker performance testing. Apply the material technology to the production of other absorptive components within an optical telescope (i.e., mounts, support struts, etc.). Investigate the potential of adapting the technology for other commercial, space, and military applications; to include those operating within less severe environments. 

REFERENCES: 

1: R. D. Seals, et al. January 1990. Oak Ridge National Laboratory, Advanced Infrared Optically Black Baffle Materials

2:  E.A. Johnson, et al. October 1991. Spire Corporation

3:  Advanced Baffle Materials Technology Development

KEYWORDS: Materials, Processing, Texturing, Baffles, Absorptive, Optics, Telescopes, Reflect, Space, Nuclear, Survivability, Sensors, Seekers 

CONTACT(S): 

Aaron Williams 

(256) 450-2851 

Aaron.williams@mda.mil 

TECHNOLOGY AREA(S): Sensors, Electronics 

OBJECTIVE: Eliminate reliance on integrated non-uniformity correction hardware in electro-optical/ infrared (EO/IR) sensors to reduce sensor size, weight, power, and cost (SWaP-C), simplify maintenance and integration, and streamline mission continuity of operations. 

DESCRIPTION: This topic seeks to explore two areas of development to address bias drift in infrared Focal Plane Arrays (FPA): 1) Next generation Read-Out Integrated Circuits (ROICs) 2) Next generation adaptive Non-uniformity Correction (NuC) algorithms for machine vision applications In the cases of both technology areas, responsive proposals will address the robustness of their approach such that the solution is applicable to any detector material, waveband independent, and not tied to a specific concept of operation or in situ data collection criteria. EO/IR sensors have become ubiquitous for not just missile defense applications, but across the DoD mission space. One of the main drives of SWaP-C is integrated hardware to address bias drift in thermal infrared (3 microns wavelengths or higher) FPAs caused by both electrical and thermal drift. These effects create both transient and fixed pattern noise that down-stream processing algorithms must contend with if not properly corrected. The need exists to emulate within the thermal infrared sensor domain the long-term stability typical of visible-near infrared FPAs. The gold standard for removing these biases is a flat-field correction which constitutes a signal-interrupt and requires in-line optical calibration hardware. Adaptive NuC algorithms have also demonstrated their utility in scenarios where SWaP is limited and/or signal-interrupts are untenable. However, these algorithms are usually optimized for human consumption and not machine vision applications and/or perform poorly under highly transient temperature conditions. The purpose of this topic is to explore methodologies at the ROIC and/or signal processing level to eliminate the effects of bias drift in the sensor’s output image such that in situ bias characterization is not dependent on in-line calibration hardware, signal interrupts, or specific data collection criteria during mission activities. Furthermore, the proposer should demonstrate the ability of their method to maintain the radiometric calibration of the FPA’s output imagery. The proposer is encouraged to demonstrate the traceability of their method across all EO/IR wavebands and detector materials, though specific wavebands and materials may be prioritized for the purpose of the Phase I/II efforts. 

PHASE I: Demonstrate the applicability of the proposed concept using modeling and simulation tools. The proposer should also reduce project risk by investigating and reporting on the feasibility of limited quantity production. 

PHASE II: Refine, update, and finalize the solution developed under Phase I. Implement the final design in a represented sensor package for the purpose of a real-time hardware demonstration at the laboratory level. 

PHASE III: Refine, update, and integrate the demonstration prototype developed under Phase II into a fieldable sensor package capable of participating in a missile defense application test to demonstrate the applicability and Technology Readiness Level of the prototype technology. The proposer should also pursue commercialization opportunities of the prototype technology and/or transition of the technology to industry, University, or Federally Funded Research & Development Center partners. 

REFERENCES: 

1: Missile Defense Agency New Technology Areas. https://www.mda.mil/system/potential_new_technologies.html

2:  Space Based Defense for Hypersonics. https://breakingdefense.com/2018/03/space-based-sensors-needed-for-missile-defense-vs-hypersonics-mda

3:  Missile Defense Space Layer. http://spacenews.com/commentary-a-space-sensor-layer-for-missile-defense

KEYWORDS: Thermal, Infrared, EO/IR, ROIC, Algorithm 

CONTACT(S): 

Jonathan Hanks 

(256) 450-1596 

Jonathan.Hanks@mda.mil 

TECHNOLOGY AREA(S): Materials 

OBJECTIVE: Develop and demonstrate enabling technologies for a liquid upper stage engine utilizing low-toxicity, non-cryogenic liquid propellants that are not based on AF-M315E or LMP-103 propellant formulations. 

DESCRIPTION: The goal of this topic is to develop and demonstrate enabling technologies for an air launch/air transportable liquid upper stage engine. Propellants may be new or existing formulations but must be non-cryogenic, non-toxic, with a hazard classification 1.3C or better, and must not be based on AF-M315E or LMP-103 propellant formulations. For design purposes, consider an engine with a thrust of 900 pounds-force (lbf) (4000 N), burn duration of 75 seconds, and a volumetric constraint of 50-inch diameter by 110-inch length. The intent of this topic is to explore innovative propellants and combinations that meet the necessary handling and transportation requirements as well as the performance requirements. Gelled propellants (or other approaches for increased viscosity and reduced vapor pressure) may also be considered but must address rheological properties to show understanding of flow behavior compared to conventional Newtonian propellants. Propellants should be able to meet standardized insensitive munitions test parameters, as defined by MIL-STD-2105D, and be insensitive to adiabatic compression. For this effort, low-toxicity is defined as materials not identified as prohibited for military air transportation per Attachment 4, Table A4.1, of Air Force Manual 24-204. Engine design and/or components may also be proposed. Key design parameters and associated components should be identified and demonstrated at a subscale level. One example would be addressing the ignition and combustion process of new and/or novel mono/bi/multi-propellants that would allow scale-up of the combustion chamber commensurate with performance parameters. 

PHASE I: Develop a proof-of-concept solution; identify candidate propellants, engine and/or component design concepts, test capabilities, and conduct initial design trade studies. 

PHASE II: Finalize the engine design, build subscale, heavyweight test hardware, and conduct a hot-fire test to validate the design. The test should show high specific performance with scalability to a high thrust, flight representative engine. 

PHASE III: Demonstrate the developed engine via further engine hot-fire testing. Conduct engineering and manufacturing development, test, evaluation, and qualification. Demonstrate in a real system or operation in a system level testbed with insertion planning for a target program. 

REFERENCES: 

1: G. P. Sutton. 2001."Rocket propulsion Elements

2:  Introduction to Engineering of Rockets." 7th edition, John Willey &Sons. MIL-STD-2105D

3:  US Insensitive Munitions Policy Update

4:  DTIC.

5:  Toxicity of Rocket Fuels: Comparison of Hydrogen Peroxide with Current Propellants

6:  http://www.gulflink.osd.mil/al_jub_ii/al_jub_ii_refs/n50en153/dmattie.htm

7:  Air Force Manual 24-204: Preparing Hazardous Materials for Military Air Shipment. 13 July 2017. http://static.e-publishing.af.mil/production/1/af_a4/publication/afman24-204/afman24-204.pdf

KEYWORDS: Upper Stage, Liquid Propellant, Rocket Engine, Thrust, Low-toxicity 

CONTACT(S): 

Claude Snoddy 

(256) 955-4251 

Claude.Snoddy@mda.mil 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop and implement, in software, a comprehensive methodology to reduce uncertainties in the modeling and simulation of high-speed aerodynamic vehicle flight. 

DESCRIPTION: The goal of this topic is to develop and implement a strategy and methodology through software for reducing the uncertainties inherent in current modeling and simulation of flight vehicles in high-speed aerodynamic flight. As the velocity of a flight vehicle increases, the importance of understanding the effect of natural environments on a system and its components becomes increasingly important. The impact of weather on high-speed flight vehicles is a complex, multidisciplinary, multi-scale problem that includes effects of wind, temperature, radiation, precipitation, and other non-water atmospheric particulates. The effects of impact-induced shock waves must also be understood in order to develop robust high-speed designs. One of the greatest sources of uncertainty is the effects of atmospheric conditions on thermal protection systems. The survivability of vehicle hardware, such as nose cones and radomes, is dependent upon surface material ablation caused by atmospheric friction, hydrometeor erosion, and abrasion from other atmospheric particles. 

PHASE I: Develop a prototype software solution for optimizing investment, across technology areas, in modeling and simulation of high-speed aerodynamic flight vehicles. The proposed software solution should be based on investigation of research and development efforts currently underway across government, industry and academia related to high-speed aerodynamic flight vehicles—particularly those efforts related to modeling and simulation of atmospheric conditions and effects such as weather and particulates, as well as phenomenology of high-speed flight such as plasma effects, shock wave interaction and material erosion. Identify and rank those technology areas where investment in research and innovation would provide the greatest benefit to government programs and capabilities. Identify the specific testing facilities and capabilities which might be available and appropriate for validating modeling and simulations with test data. The software solution should inform a strategy for investment across technology areas which will optimize advancement and maximize confidence in modeling and simulation of high-speed aerodynamic flight vehicles. Include a plan for implementing the approach and prototype software solution in Phase II. 

PHASE II: Implement the plan, test and refine software developed during Phase I. The expected outcome of the Phase II effort is a software solution demonstrating the tools and technologies required for uncertainty quantification of high speed vehicle flight calculations. Validate where possible and feasible under the Phase II. Where validation is not performed, define the requirements, and develop a plan for validating the technology in Phase III. Demonstrate the computational tools developed in a relevant environment showing mission planning and engagement scenarios. 

PHASE III: Develop military and industry partnerships to proliferate the technology in support of higher speed missile defense systems. The development of an uncertainty quantification tool for higher speed vehicle flight behavior could have applicability across several missile defense applications and several government agencies. 

REFERENCES: 

1: E. M. Kraft. "The Air Force Digital Thread/Digital Twin - Life Cycle Integration and Use of Computational and Experimental Knowledge." 54th AIAA Aerospace Sciences Meeting, AIAA SciTech Forum

2:  (AIAA 2016-0897)

3:  M. Jemison, M. Sussman, and M. Arienti. 2014. Compressible, multiphase semi-implicit method with moment of fluid interface representation. Journal of Computational Physics. Vol. 279

4:  pp 182-217

5:  B.E. Moylan. 2010. Raindrop Demise in a High Speed Projectile Flowfield – A Dissertation. The University of Alabama, Huntsville

6:  R.K. Shukla. 2014. "Nonlinear preconditioning for efficient and accurate interface capturing in simulation of multicomponent compressible flows." Journal of Computational Physics. Vol. 276

7:  pp 508-540

8:  P. Ginoux, M. Chin, I. Tegen, J. M. Prospero, B. Holben, O. Dubovik, and S.J. Lin. 2001. Sources and distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research. Vol. 106

9:  Issue D17

10:  pp 20255-20273

KEYWORDS: High-speed Flight, Uncertainty Quantification, Ground-test, Atmospheric Sand, Missile, Probabilistic Weather, Radome 

CONTACT(S): 

Claude Snoddy 

(256) 955-4251 

Claude.Snoddy@mda.mil 

TECHNOLOGY AREA(S): Info Systems, Sensors, Battlespace 

OBJECTIVE: Develop an open architecture and roadmap for feasible migration to interoperable Command, Control and Intelligence (C2I) and Modeling and Simulation (M&S) systems based on an open application programming interface gateway service, open geospatial standards, and interface control. 

DESCRIPTION: The United States Special Operations Command (USSOCOM) requires technologies that support end-to-end flow and provenance of large volumes of information enabling decision makers the ability to understand the context and evaluate courses of action based on the veracity and value of data. These systems require automated interoperability to synthesize data/information so that commanders can determine the tools necessary to address issues and to anticipate and mitigate tactical, operational and strategic surprises. Special Operations Forces (SOF) require course of action analysis and mission rehearsal as an integrated capability supporting no notice, short notice special operations missions. Standards-based automated interoperability of C2I and M&S geospatial data will enable SOF to evaluate alternate courses of action and tactics, techniques and procedures as well as unconventional approaches to executing special operations missions wherever they arise geographically. Recent advances in algorithmic development, imagery exploitation, high performance computing, and emerging standards enable integration of systems and technologies to facilitate automation of C2I and M&S interoperability. These advances build on early interoperability experiments among university, university-affiliated and Federally funded research and development centers and non-profit standards development organizations. Such advances promise to satisfy long-standing USSOCOM requirements for rehearsal of special operations missions including tactics and situational awareness (understanding real world combat environment), decision making and coordination. The goal is for SOF to rehearse and evaluate specific special operations missions after completion of detailed mission planning in an effort to minimize the probability of detection, risk and combat losses during the actual mission while maximizing the probability of success. The ideal system will facilitate the interface between data (e.g. operations, intelligence, environmental) and the users of the data, making maximum use of existing data to prevent duplication. It will help assure, to the maximum extent possible, commonality and/or compatibility of the source data used in each phase of planning, mission rehearsal or mission execution. A common system for both real and simulated data (day/night visual/sensors, radar, moving model and electronic combat) is required for integrated mission rehearsal of all SOF elements. Interoperability of command data will facilitate development of a modular open system architecture that can incorporate emerging technology to meet future SOF requirements for analysis, mobility, and autonomous systems. 

PHASE I: Conduct a feasibility study to assess what is in the art of the possible that satisfies the requirements specified in the above paragraph entitled “Description.” The objective of this SOCOM Phase I STTR effort is to generate and/or collect sample data, appropriately labeled for testing, and provide an assessment of compatibility for automated interoperability of C2I and M&S geospatial data within the given trade space that will satisfy SOCOM requirements for seamless mission planning and rehearsal anywhere in the world on short notice. The objective of this USSOCOM Phase I SBIR effort is to conduct and document the results of a thorough feasibility study to investigate what is in the art of the possible within the given trade space that will deliver the described technology. The feasibility study should investigate all known options that meet or exceed the minimum performance parameters specified in this write up. It should also address the risks and potential payoffs of the innovative technology options that are investigated and recommend the option that best achieves the objective of this technology pursuit. The funds obligated on the resulting Phase I SBIR contracts are to be used for the sole purpose of conducting a thorough feasibility study using scientific experiments and laboratory studies as necessary. Operational prototypes will not be developed with USSOCOM SBIR funds during Phase I feasibility studies. Operational prototypes developed with other than SBIR funds that are provided at the end of Phase I feasibility studies will not be considered in deciding what firm(s) will be selected for Phase II. 

PHASE II: Develop a detailed design, conduct interoperability experimentation under varying operating conditions with varying sensor types, and demonstrate a prototype implementation for government use of all critical technologies and attributes using open geospatial standards. 

PHASE III: Numerous potential applications exist within defense and civilian commercial markets. The USSOCOM objective is similar to Service challenges within the US Department of Defense as well as allied and Partner Nation forces. Automated interoperability of geospatial data is required for autonomous, shared battlespace awareness across warfighting functions. Technologies and standards prototyped in this STTR may be used in various applications beyond defense including humanitarian assistance, disaster relief, law enforcement, security, and virtual or mixed reality gaming. 

REFERENCES: 

1: K. Bentley, M. Hill, M. Johnson, R. Moore, "GeoPackage: Unifying Modeling and Simulation with Mission Command Geospatial Data," 2017. http://exhibits.iitsec.org/2017/public/SessionDetails.aspx?FromPage=Sessions.aspx&SessionID=4663&SessionDateID=56

2:  R. Chartrand, R. Keisler, T. Kelton, D. Raleigh, S. Skillman, M. Warren, "Data-Intensive Supercomputing in the Cloud: Global Analytics for Satellite Imagery in Proceedings of the 7th International Workshop on Data-Intensive Computing in the Cloud (DataCloud ’16)," IEEE Press, 24-31, 2016. https://arxiv.org/pdf/1702.03935.pdf

3:  B. Comer, "SE Core Geospatial Data in Mission Command and Modeling & Simulation," 2017. https://portal.opengeospatial.org/files/?artifact_id=76888

4:  R. Exler, S. Simmons, "How Data Standards Could Speed Geospatial Data Sharing," 2015. https://www.c4isrnet.com/intel-geoint/2015/07/22/how-data-standards-could-speed-geospatial-data-sharing/

5:  F. Fatima, S. Ali, M.U. Ashraf, "Risk Reduction Activities Identification in Software Component Integration for Component Based Software Development (CBSD)," 2017. http://www.mecs-press.org/ijmecs/ijmecs-v9-n4/IJMECS-V9-N4-3.pdf

6:  The Goldman Sachs Group, "Artificial Intelligence: AI, Machine Learning and Data Fuel the Future of Productivity," 2017. https://tech.fpt.com.vn/wp-content/uploads/2017/12/tai-lieu.pdf

7:  M. Heib, D. Maxwell, "Converging Simulation and C2: Improving Foundation Data Consistency and Affordability," 2012. http://www.kadsci.com/pdf/Converging%20Simulation%20&%20C2.pdf

8:  L. Khimeche, J.M. Pullen, "Advances in Systems and Technologies Toward Interoperating Operational Military C2 and Simulation Systems," 2014. http://www.dtic.mil/get-tr-doc/pdf?AD=ADA606909

9:  Lawrence Livermore National Laboratory, "A Converging Path for Simulation, Machine Learning, and Big Data," 2016. https://computation.llnl.gov/newsroom/simulation-machine-learning-big-data-converging-path

10:  NASA Technology Roadmaps, TA-11, "Modeling, Simulation, Information Technology, and Processing," 2015. https://www.nasa.gov/sites/default/files/atoms/files/2015_nasa_technology_roadmaps_ta_11_modeling_simulation.pdf

11:  M. Peck, "Army Developing Military Grade Google Earth," 2016. https://www.govtechworks.com/army-developing-military-grade-google-earth/#gs.xjckm2E

12:  J. M. Pullen, L. Khimeche, R. Wittman, B. Burland, J. Ruth, J.I Hyndøy, "Coalition C2-Simulation History and Status," 2014. http://netlab.gmu.edu/pubs/NMSG%20Symposium%202014%20paper%201.pdf

13:  L. Winters, M. Gorman, A. Tolk, "Next Generation Data Interoperability: It’s all About the Metadata," 2006. https://www.researchgate.net/publication/255624333_Next_Generation_Data_Interoperability_It's_all_About_the_Metadata

KEYWORDS: Interoperability, Machine Learning, Artificial Intelligence, Autonomous Systems, High Performance Computing, Conceptual Data Models, Metadata 

CONTACT(S): 

sbir@socom.mil 

TECHNOLOGY AREA(S): Materials 

OBJECTIVE: Produce rubber from plants and implement alternative agronomic practices conducive to environmental conditions for increased rubber biosynthesis, extraction, processing, and manufacturing. 

DESCRIPTION: There is a compelling DoD need to develop a uniform and reliable alternative natural rubber product for industrial and military use that will achieve the following: improve the amount, quality, and value of natural rubber production outcomes; increase the cultivability or environmental tolerance to rubber yield; identify and address issues associated with natural rubber extraction and/or natural rubber processing, at any step; and define and determine factors that cause rubber biosynthesis homogeneity between plants for dependable natural rubber production, and further alternative natural rubber crop performance goals. Rubber is a valuable natural resource, an $18 billion industry, and natural rubber is in particularly high demand. Natural rubber’s global consumption is approximately 11 million metric tons per year with the United States importing over 1 million metric tons per year. Thailand, Indonesia, Malaysia, India, Vietnam and China are the world’s top six natural rubber producers consisting of ~86% of global natural rubber production. In line with this, the world’s leading natural rubber manufacturers are mainly located in Southeast Asia. Currently, the vast majority of natural rubber is derived from the para rubber tree, Havea brasiliensis. To harvest isoprene polymers (the natural product), incisions are made into the bark allowing the crude product to drip into individual containers for collection and sale. The collected material is then sold to facilities for further processing. While the product refinement process has evolved over the years, growth and harvest methods have remained largely unchanged. Low genetic diversity, highly localized farms, and tapping for latex contribute to disease susceptibility, outbreak, and spread. In contrast, synthetic rubber is sourced from petroleum. Crude oil is refined and combined with natural gas to form monomers. The monomers are sold by refineries to synthetic rubber facilities where chemical agents are used to form chains of petroleum monomers, known as synthetic polymerization. Unfortunately, synthetic rubber is incompatible with many uses because it is less compression and impact resistant than natural rubber. Additionally, natural rubber has increased tear and tensile strength, as well as, superior mechanical performance properties. For example, aircraft tires are made of natural rubber; whereas, synthetic rubber (which is less expensive to procure) is used for less demanding products. Some military grade rubber products, machinery, and equipment are in line with natural rubber’s properties having unique specifications for operation and remaining functional in unique conditions (e.g., high impact of an aircraft landing, shock of an inordinately heavy/large automobile driving in rough terrain). 

PHASE I: Proposers are encouraged to use emerging bioengineering tools to achieve the stated objective, and ensure proposed teams have relevant experiences or insights moving technology from lab to market. Determine feasibility of the stated objective. Example approaches could include one or more of the following: • Develop novel approaches to improve rubber biosynthesis in alternative plants. • Increase levels and/or increase quality of natural rubber. • Improve natural rubber extraction and processing. • Mitigate environmental factors which limit growth and yield. Expected outcomes could be: the design and development of an innovative natural rubber production concept, along with the limited testing of materials to determine technical feasibility, and performing key component technological milestones. Develop success criteria that aligns with market standards (e.g., high tensile strength, high elongation value, abrasion resistant). Conduct a small scale demonstration of the proposed approach to determine if expected outcome is achieved. Characterize the relevant physical and biochemical properties of the natural rubber alternative product; match or exceed the necessary parameters through optimization. The deliverable is a Phase I final report that includes a commercialization strategy that identifies the commercial and military entities currently involved in, or needed, for transitioning the technology from lab to market. 

PHASE II: Construct an operating prototype predominantly consisting of alternative natural rubber material. Use Phase I test results to further develop prototype for fabrication and prototype performance analysis in Phase II. Prior to field testing the prototype, commercial and/or defense partners must be engaged to establish metrics of success relative to the state-of-the-art (i.e., recognized performance abilities of natural rubber). Required Phase II deliverables will include: an operational prototype that incorporates field test results, quarterly reports containing an updated market analysis, product hypothesis, basic cost model, and go-to-market strategy. 

PHASE III: Phase III: (Commercial): A commercial application of a developed alternative natural rubber resource is the substitution of its use in the natural rubber supply chain. Some examples include replacing an industrial plantation, smallholder farmer, or implementing improved latex collection/drying/shredding of the alternative rubber product to increase its commercial potential. Performers are expected to leverage relationships established during Phase II to acquire funding from the private sector in Phase III. The goal of Phase III is successful commercialization of alternative natural rubber by testing product performance in the entire pipeline; extraction to manufacturing at scale. Co-products of alternative natural rubber extraction (e.g., lower molecular weight rubber) can be placed into the business model. Establish roles of commercial partners to secure path forward of bringing product to market. Present product performance data to manufacturers, end users, and decision makers within the supply and distribution chain. Required Phase III deliverables will include quarterly reports, progressively comprehensive, describing a detailed and partner-validated technology transition plan, and obtaining commitments from necessary parties for continuation of support after the STTR program. Phase III (Military): A military application of a developed alternative natural rubber resource is the incorporation if its use in the processing and logistics of military products derived from natural rubber. Some examples include natural rubber distributors selling to processing plants for military grade natural rubber products (e.g., aircraft tire fabrication) or natural rubber processing plants incorporating the alternative natural rubber resource into other military rubber products. Performers are expected to develop a business model for alternative natural rubber and rubber co-products for use in military grade rubber products that meet and exceed the specifications set forth by the US military. The goal of Phase III is to explore, test, and establish the utility of alternative natural rubber in the supply chain of any military ground transportation vehicle components, aerospace goods, marine vessel applications (e.g., ships, submarines), armaments and munitions components, and other high-performance military applications. Perform stress-strain pressure tests of prototypes and applicable performance metrics defined by the end user to validate its entry into the military positioned supply chain. Required Phase III deliverables will include quarterly reports describing the increased efficiency and durability of prototypes containing alternative natural rubber. 

REFERENCES: 

1: Van Beilen, J. B., Poirier, Y. 2007. Establishment of new crops for the production of natural rubber. Trends in Biotechnology 25:522-529

2:  Davis, W. 1997. The rubber industry’s biological nightmare. Fortune 4 August, pp. 86–95

3:  Mooibroek, H., Cornish, K. 2000. Alternative sources of natural rubber. Appl Microbiol Biotechnol 53:355-365

4:  Cornish, K., 2017. Alternative natural rubber crops: why should we care? Technology and Innovation 18:245-256

KEYWORDS: Natural Rubber, Plant, Biosynthesis, Natural Rubber Plants, Natural Rubber Crops 

CONTACT(S): 

Dr. Blake Bextine 

blake.bextine@darpa.mil 

TECHNOLOGY AREA(S): Chem, Bio_defense, Bio Medical 

OBJECTIVE: Develop an integrated computational platform that fuses multiple phenotypic data and applies machine learning algorithms to identify the pathogenic potential of bacterial samples based on phenotype alone. 

DESCRIPTION: There is a compelling DoD need to develop a computational platform that appropriately combines phenotypic data into a form that is amenable to machine learning (ML) methods, and uses these data to assess the pathogenic potential of bacteria. Bacterial pathogens represent a significant and growing risk to global health and security. Previously unknown (and potentially engineered) species with little genetic similarity to known pathogens, and/or for which little genomic information is available, are of particular concern. New experimental approaches for identifying these pathogens using only the pathogens’ behavior or phenotype are being developed; however, we currently lack computational tools that can process the diverse phenotypic data—host response, niche preference, cell/surface attachment, exotoxin production, antibiotic resistance, etc.—into a form that can enable predictions of pathogenicity in the absence of -omic data. Additionally, algorithms must be improved so that they can use phenotypic data to predict pathogenicity. ML methods hold considerable promise, having already shown some measure of success in analyzing epigenetic, genetic, and proteomic data to score the pathogenic potential of bacteria [1-3]. 

PHASE I: Describe an approach to algorithm training and provide sufficient support for any claims regarding its ability to identify previously unknown pathogens. Describe gaps in existing methods for phenotypic data fusion and ML of biological data and the specific advances offered by the proposed approach. Quantitative measures of expected performance are encouraged. Proposed platforms must be generally flexible with respect to the number and type of data, be able to incorporate data from a wide range of instruments, and deal with a variable number of samples. At a minimum, platforms must be able to incorporate and operate on data related to the three main categories of pathogenic traits: (1) niche finding (e.g., ability to adhere to host tissues); (2) ability to harm a host (e.g., secretion of exotoxins to damage host cells); and (3) self-preservation (e.g., ability to evade the immune system). Demonstrate integration of phenotypic data for both known pathogens and non-pathogens into computational structures amenable to ML. (Data may be curated from the scientific literature and/or provided by partner labs, as needed.) In constructing a ML training dataset, choices of data sources and types must be justified. Demonstrate ability to identify bacteria from a set of well-known pathogens and non-pathogens with =90% accuracy. End-of-Phase I deliverables include a final report describing the training data and data sources (e.g., literature, partner labs, etc.), an implementation of the ML algorithm, the training data itself, and all relevant documentation related to the underlying architecture and use of the platform. Final report must include sufficient detail to demonstrate performers’ understanding of the overall field and the needs of potential government and/or commercial end-users. The expected Technology Readiness Level (TRL) to be achieved at the end of Phase I should be between 3 and 4. 

PHASE II: Incorporate methods for rapid ingestion of new data and demonstrate expandability of the platform through the addition of new results and new data types generated by the research community during Phase I. Demonstrate quantifiable improvements in identification accuracy and speed for known pathogens. Demonstrate application to sample species for which there has been no definitive identification of pathogen or non-pathogen, with suggestions as to experiments that should be done to increase identification confidence. Establish a roadmap that describes the path(s) to transition and use in applications. Importantly, platforms must be accessible to end-users with little or no computational experience. The main Phase II deliverable is a final report that includes details on all improvements made to the platform (with quantitative comparison of performance), descriptions of data, results of any applications to sample species, and the transition roadmap. The algorithm itself (with appropriate documentation) and any additional data must also be delivered by the end of Phase II. Technology Readiness Level (TRL) of 5 is expected to be achieved by the end of Phase II. 

PHASE III: A successful computational platform for phenotype-based pathogen identification has significant potential to transition to the commercial sector for use in DoD and government applications. Potential customers include government agencies with interests in biological threat identification and countermeasure development, as well as government and non-government partners focused on global health. Technology developed under this STTR, if successful, may also be transitioned to performers and government partners working on the DARPA Friend or Foe program to complement existing efforts to identify pathogens based on phenotype. Friend or Foe transition will be facilitated by DARPA as deemed appropriate. 

REFERENCES: 

1: Burstein, David, et al. "Uncovering the Legionella genus effector repertoire-strength in diversity and numbers." Nature Genetics 48.2 (2016): 167.

2:  Carter, Michelle Qiu. "Decoding the ecological function of accessory genome." Trends in Microbiology 25.1 (2017): 6-8.

3:  Deneke, Carlus, Robert Rentzsch, and Bernhard Y. Renard. "PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data." Scientific Reports 7 (2017): 39194.

KEYWORDS: Bacteria; Pathogen; Phenotype; Machine Learning; Data Fusion 

CONTACT(S): 

Dr. Paul Sheehan 

paul.sheehan@darpa.mil 

Dr. Kristen O’Connor 

Mr. Richard Mathieson 

TECHNOLOGY AREA(S): Chem, Bio_defense 

OBJECTIVE: Develop and demonstrate semiconductor-based compact solar-blind deep-UV avalanche photo-detectors (APDs) operating at 200 – 250 nm wavelength region. 

DESCRIPTION: There is a compelling DoD need to develop compact, portable chemical and biological identification systems, which are of significant interest within both the commercial and military sectors. Systems deployed today provide critical protection for warfighters in the field. Deep UV detectors operating in 200 – 250nm regime enable the detection of several harmful bio-chemical species and explosives while avoiding the background solar radiation. While progress has been made on miniaturizing laser sources for such systems, due to a variety of issues, the required deep UV detection elements remain inefficient, bulky, and have low sensitivity. Recent advances in semiconductor-based deep UV avalanche photodetectors promise high sensitivity with photon counting capability in small form factor for portable applications in the field. Several deep UV APDs have been reported using silicon and AlGaN/SiC materials [Ref 1]. However, current solutions for deep UV APDs have difficulty exhibiting high quantum efficiency, low dark current, and high multiplication gain with Geiger-mode capability of single photon detection and solar blindness to solar radiation without elaborative coatings. 

PHASE I: Conceptualize and design an innovative device to demonstrate ultra-high gain solar-blind APD devices and fine-pitch imager arrays to operate from 200 nm – 250nm. The proposed APD devices should provide high extrinsic quantum efficiency (>70%) with high multiplication gain (>10E6) for photon-counting with low dark current (<0.1nA). The deep-UV APDs should provide high avalanche gain with possible single-photon detection when operating in Geiger mode. Integrated APD arrays with dense pitch and filling factor are needed to provide spectral analysis to detect and distinguish bio-chemical species and signatures. Demonstrate associated fabrication process of test structures, material selection and growth preparation, device models, and simulations, the ability to produce high gain low noise semiconductor solar-blind avalanche photo diodes to be operated in linear mode and Geiger-mode over the 200 – 250nm range. Devices designed in this Phase should validate models and simulations and show a path to meet the efficiency, gain and dark current targets of Phase II. Phase I demonstrations of solar-blind deep UV APD concepts are expected to reach a Transition Readiness Level of 2-3. 

PHASE II: Using designs of Phase I, develop, fabricate and demonstrate solar-blind deep UV avalanche photo diodes and arrays by acquiring or synthesizing the necessary materials to fabricate and solar-blind APDs in linear mode and Geiger mode over 200 – 250nm with high quantum efficiency (>70%), high multiplication gain (>10E6), and low dark current (<0.1nA). Fabricate, demonstrate and test a deep UV APD imager array with 16x16 elements. Deliver a prototype of the imager array and a detailed report that will include prototype design, demonstration and test results of the Phase II effort. Phase II demonstrations of the deep UV APD imager array are expected to achieve a Transition Readiness Level of 4-5. 

PHASE III: Mature and transition the Phase 2 prototype devices to be compatible with an identified transition platform. Such devices should be informed through work with military stakeholders to design-in and qualify the technology. The developed technology is suitable for dual-use applications which are expected to find a wide range of commercial and industrial bio-chemical detection applications. 

REFERENCES: 

1: S. Nikzad, M. Hoenk, A. Jewell, J. Hennessy, A. Carver, T. Jones, T. Goodsall, E0. Hamden, P. Suvarna, J. Bulmer, F. Shahedipour-Sandvik, E. Charbon, P. Padmanabhan, B. Hancock, and L. Douglas Bell, "Single photon counting UV solar-blind detectors using silicon and III-nitride materials," Sensors 2016, 16(6), 927, 2016.

KEYWORDS: Solar-blind Detector, Deep UV APD, APD Array, Geiger Mode APD, Bio-chem Detection, UV Spectra-meter 

CONTACT(S): 

Dr. Young-Kai Chen 

young-kai.chen@darpa.mil 

TECHNOLOGY AREA(S): Bio Medical, Human Systems 

OBJECTIVE: Develop mechanical or biologically engineered solutions to revolutionize reliable, autonomous, multi-hormonal glycemic control to improve patient outcomes stemming from traumatic organ injury and loss, critical illness, and long-term therapeutic maintenance. 

DESCRIPTION: Traumatic pancreatic injury or loss (about 7% of battle injuries) [1], and various pancreatic pathologies can cause glucose abnormalities that significantly affect patient morbidity and mortality. In addition, traumatic injuries of other organs, infection and other non-pancreatic illnesses also disturb normal blood glucose levels, which likewise negatively impact patient outcomes [2-9]. There is thus a compelling DoD need to improve automated blood glucose control in such cases in order to reduce mortality, duration of illness and ameliorate long-term outcomes [10]. Blood glucose levels are tightly controlled by the pancreas’ multi-hormonal regulatory system; dysregulation can cause coma, death, organ injury and failure, and a reduction in the body’s ability to heal itself. It is well established that outcomes for patients suffering emergent or pathological loss of pancreas function are associated with an increase in patient morbidity, mortality, duration of hospital stay, as well as long-term health outcomes. It is also well known that stress-induced glycemic abnormalities, general glucose intolerance, and insulin resistance are common among critically ill patients, including those without a diagnosis of diabetes. Data suggests that a restoration of euglycemia in critically ill patients significantly lowers morbidity and mortality in the surgical ICU and morbidity in the medical ICU. Glycemic regulation is also impacted by burns and other trauma incurred by the warfighter, thus an automated means to control glycemic levels is especially critical for far forward medical operations, as well as to treat pathologic hyperinsulinism, diabetes and all varieties of secondary insulin dysfunction. While recent developments in blood glucose monitoring have led to improved patient diagnosis and care, treatment has continued to be hampered by the lack of accurate interpretation, constant continuous treatment, and the inability to imitate pancreatic function through multi-hormonal therapy. At present no technology exists to autonomously regulate abnormal glycemia in these patients. This project seeks the creation of autonomous, multi-hormonal systems, either mechanical - utilizing machine learning techniques, or biological engineering solutions capable of reestablishing glycemic balance in both the critical care environment, and in patients with long-term pathologies. Solutions should seek to avoid the use of immune-suppressants as a part of their solution. A combined mechanical biological solution with embedded machine learning techniques is acceptable as well. The program manager has identified this topic as research involving Human and/or Animal use. In accordance with DoD policy, human and/or animal subjects in research conducted or supported by DARPA shall be protected. Although these protocols may not apply to Phase I research activities, proposers should be aware that significant lead time is required to prepare the documentation and obtain approval. Please visit http://www.darpa.mil/work-with-us/for-small-businesses/participate-sbir-sttr-program and click on the Human Research Guidelines link or the Animal Research Guidelines link to understand what is required to comply with human protocols and animal protocols in order to avoid delays in awards. Further, proposers are encouraged to separate research tasks and tasks involving human and/or animal use in the Technical Volume and Cost Volume in order to avoid delay of contract award. 

PHASE I: Mechanical / computational approaches must demonstrate the ability to continuously adapt delivery of hormones or drugs based on a patient's rapidly changing blood sugar levels. Approaches must include an autonomous, closed loop system capable of sensing blood glucose levels and delivering appropriate hormones or drugs to assert proper glycemic control. Biologically engineered approaches must demonstrate the ability to produce an autologous, multi-hormonal pancreatic organoid consisting (at a minimum) of pancreatic alpha and beta cells, for use as the solution to, or as the basis for improving long-term management and outcomes. Mechanical / Computational Phase I Deliverables: 1. Generation of biocompatible device specifications, including Machine Learning (ML) / adaptive control algorithms. 2. Development of proof-of-concept prototype. 3. Prototype should be tested against and meet design specifications. Device should demonstrate capability of injecting the correct hormone / drug based on glycemic environment. Biologically Engineered Phase I Deliverables: 1. Generation of viable, human autologous pancreatic organoids consisting (at a minimum) of pancreatic alpha and beta cells from IPSC’s or Stem cells. 2. Provide evidence (morphological, histological, biochemical) that organoids produce appropriate hormones based on glycemic environment. Both mechanical and biological projects should provide a plan for testing; biological projects must specify plans for improving Phase II yield to achieve sufficient organoids for testing. 

PHASE II: Biologically engineered approaches will continue refinement of autologous, multi-cell type organoid production, viability, and function, while the mechanical/computational approaches will refine the algorithms based on studies. Both approaches will conduct preclinical or clinical feasibility studies as appropriate. Mechanical / computational Phase II Deliverables 1. Prepare regulatory documentation related to FDA (IND) submission as appropriate. 2. Pre-clinical or clinical feasibility studies as appropriate (safety, efficacy). 3. Refine reliable, multi-hormonal (or multi-drug) autonomous glycemic control system based on results of preclinical and/or clinical feasibility studies. In particular, improve ML/adaptive control algorithms as indicated. 4. Documentation and/or publication of results from pre-clinical or clinical feasibility studies. Biologically Engineered Phase II Deliverables: 1. Improved production of Organoids to levels that enable preclinical studies 2. Verification of hormonal response to glycemic environment 3. Estimate size and number of organoids required for efficacious preclinical studies. 4. Method of biocompatible delivery in test subject (e.g., encapsulated subcutaneous implantation, direct in vivo implantation, or via scaffold implantation). 5. Prepare regulatory documentation related to FDA (IND) submission as appropriate. 6. Pre-clinical or clinical feasibility studies as appropriate (safety, efficacy). 7. Documentation and/or publication of results, if relevant preparation for FDA approval. 

PHASE III: If successful, the glycemic control method developed in this STTR is equally applicable for far-forward medical operations treating the wounded soldier in battle as well as in the treatment of civilian illness and emergency situations. Targeted DoD programs and missions, identified in Phase II, will be given the opportunity to apply the device. 

REFERENCES: 

1: Surgical Management of Modern Combat-Related Pancreatic Injuries: Traditional Management and Unique Strategies," MAJ Amy Vertrees, MC USA

2:  CAPT Eric Elster, MC USN

3:  Rahul Jindal, MD, PhD, MBA

4:  Camillo Ricordi, MD

5:  COL Craig Shriver, MC USA, MILITARY MEDICINE, 179, 3:315, 2014

6:  Wolfe RR, Allsop JR & Burke JF. 1979 Glucose metabolism in man: responses to intravenous glucose infusion. Metabolism. 28, 210-20.

7:  Wolfe RR, Herndon DN, Jahoor F, Miyoshi H & Wolfe M. 1987 Effect of severe burn injury on substrate cycling by glucose and fatty acids. N Engl J Med. 317, 403-8.

8:  Shangraw RE, Jahoor F, Miyoshi H, Neff WA, Stuart CA, Herndon DN & Wolfe RR. 1989 Differentiation between septic and postburn insulin resistance. Metabolism. 38, 983-9.

9:  Mizock BA. 1995 Alterations in carbohydrate metabolism during stress: a review of the literature. Am J Med. 98, 75-84.

10:  Thorell A, Nygren J & Ljungqvist O. 1999 Insulin resistance: a marker of surgical stress. Current Opinion in Clinical Nutrition & Metabolic Care. 2, 69-78.

11:  Capes SE, Hunt D, Malmberg K, Gerstein HC. 2000 Stress hyperglycaemia and increased risk of death after myocardial infarction in patients with and without diabetes: a systematic overview. Lancet. 355, 773-8.

12:  McCowen KC, Malhotra A, Bistrian BR. 2001 Stress-induced hyperglycemia. Crit Care Clin. 17, 107-24.

13:  Cely CM, Arora P, Quartin AA, Kett DH, Schein RMH. 2004 Relationship of baseline glucose homeostasis to hyperglycemia during medical critical illness. Chest. 126, 879-87.

14:  Van den Berghe G. 2004. How does blood glucose control with insulin save lives in intensive care? Journal of Clinical Investigation. 114, 1187.

15:  Palmer BF & Clegg DJ. 2015 Electrolyte and Acid-Base Disturbances in Patients with Diabetes Mellitus. N Engl J Med. 373, 548-559.

16:  Takala J, Ruokonen E, Webster NR, Nielsen MS, Zandstra DF, Vundelinckx G, & Hinds CJ. 1999 Increased mortality associated with growth hormone treatment in critically ill adults. N Engl J Med. 341, 785-92.

17:  Krinsley JS. 2003 Association between hyperglycemia and increased hospital mortality in a heterogeneous population of critically ill patients. Mayo Clinic Proceedings. 78, 1471-1478).

18:  Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M, Vlasselaers D, Ferdinande P, Lauwers P & Bouillon R. 2001 Intensive insulin therapy in critically ill patients. N Engl J Med. 345, 1359-1367.

19:  Van den Berghe G, Wouters PJ, Bouillon R, Weekers F, Verwaest C, Schetz M, Vlasselaers D, Ferdinande P & Lauwers P. 2003 Outcome benefit of intensive insulin therapy in the critically ill: insulin dose versus glycemic control. Critical Care Medicine. 31, 359-366.

20:  Van den Berghe G, Wilmer A, Hermans G, Meersseman W, Wouters PJ, Milants I, Van Wijngaerden E, Bobbaers H & Bouillon R. 2006 Intensive insulin therapy in the medical ICU. N Engl J Med. 354, 449-461.

21:  Van den Berghe G, Wilmer A, Milants I, Wouters PJ, Bouckaert B, Bruyninckx F, Bouillon R & Schetz M. 2006 Intensive insulin therapy in mixed medical/surgical intensive care units. Diabetes. 55, 3151-3159.

KEYWORDS: Hypoglycemia, Hyperglycemia Diabetes 

CONTACT(S): 

Dr. Hava Siegelmann 

hava.siegelmann@darpa.mil 

TECHNOLOGY AREA(S): Ground Sea 

OBJECTIVE: Develop an ocean methane capture and electrical energy recovery system capable of supporting unmanned underwater vehicle (UUV) power storage, usage, and recharge. 

DESCRIPTION: There is a compelling DoD need for innovative approaches to methane collection and conversion for use on a UUV. The primary limitation to UUV operations is the energy storage capabilities within the usable volume of the UUV. Oceans naturally produce two types of geological methane; (a) abiotic methane originated in volcanic and geothermal systems and (b) methane derived from hydrocarbon generation processes in sedimentary basins. These naturally occurring gases can form a means for UUV energy. The ability for a UUV system to collect, store and convert methane in the ocean environment would provide a refueling capability for extended UUV operations without human interaction. Scientists are discovering a large number of gaseous methane plumes resulting from the decomposition of methane hydrates. This phenomenon will continue as ocean temperatures continue to rise. Methane plumes are common and over 165 methane plumes have been mapped off the coast of Oregon and Washington State. The ability to leverage this source of energy for UUV extended operations is of great interest to both commercial and military operators. 

PHASE I: Determine the technical feasibility of ocean methane collection, storage and conversion to power technique, and a conceptual design compatible with a UUV. Examine the forms of methane available, the energy density that can be obtained, conversion methodologies, and a means of transferring this energy to a UUV. Approaches may be either seabed infrastructure with a concomitant mating/docking/refueling/recharging/power transfer capability or designs fully onboard a UUV. The Phase I final report must include an examination of the availability of convertible ocean methane, the means to convert oceanic methane to a UUV-useable form and a conceptual design of power transfer to a UUV, and an assessment of the practicality of such an approach for both military and commercial applications. 

PHASE II: Develop a detailed design and conduct a demonstration of methane collection, storage and power conversion and a design for UUV docking/refueling/recharging if required. Demonstration must be of sufficient scale to demonstrate methane collection, storage and power conversion. Designs for a UUV docking/refueling/recharging capability must be readily adaptable to existing UUVs. Final report must include a concept of operations supported with calculations outlining UUV operations and the benefit achieved by such a methane power conversion capability. Phase II deliverables include a detailed design of system concept showing applicability to UUVs currently in use by the DoD, test readiness review documentation (at least 30-day prior to in-water demonstration), quicklook results of demonstration (within 2 weeks of completion of test) and a final report including CONOPS. 

PHASE III: Commercial: The Oil and Gas industry use UUVs to perform bottom surveys of future installation sites as well as inspection services of existing underwater infrastructure. An extended operation UUV, capable of refueling itself from the oceanic methane emissions near drilling sites would provide significant cost reductions to commercial operations. Military: UUVs are a common workhorse for the military. Missions scanning and surveying the seafloor for objects, mines, search and rescue as well as future concepts for positioning, navigation and timing. Energy storage on current UUVs often leaves mission endurance such applications at hours or a day or two before recovery operations are required. Extending operations through an organic self-refueling capability would extend operations and increase military utility for such vehicles. 

REFERENCES: 

1: Etiope, G., Lassey, K. R., Klusman, R. W. & Boschi, E. Reappraisal of the fossil methane budget and related emission from geologic sources. Geophys. Res. Lett. 35, L09307, https://doi.org/10.1029/2008GL033623 (2008).

2:  Michael J. McAnulty, Venkata G. Poosarla, Kyoung-Yeol Kim, Ricardo Jasso-Chávez, Bruce E. Logan & Thomas K. Wood. Electricity from methane by reversing methanogenesis. Nature Communications, Vol 8. 17 May, 2017. https://www.nature.com/articles/ncomms15419#supplementary-information

3:  Yuji Sano, Naoya Kinoshita, Takanori Kagoshima, Naoto Takahata, Susumu Sakata, Tomohiro Toki, Shinsuke Kawagucci, Amane Waseda, Tefang Lan, Hsinyi Wen, Ai-Ti Chen, Hsiaofen Lee, Tsanyao F. Yang, Guodong Zheng, Yama Tomonaga, Emilie Roulleau & Daniele L. Pinti. Origin of methane-rich natural gas at the West Pacific convergent plate boundary. Scientific Reports, volume 7, Article number: 15646 (15 November 2017), https://www.nature.com/articles/s41598-017-15959-5#Bib1. doi:10.1038/s41598-017-15959-5

4:  Jarad A. Mason, Julia Oktawiec, Mercedes K. Taylor, Matthew R. Hudson, Julien Rodriguez, Jonathan E. Bachman, Miguel I. Gonzalez, Antonio Cervellino, Antonietta Guagliardi, Craig M. Brown, Philip L. Llewellyn, Norberto Masciocchi, Jeffrey R. Long. Methane storage in flexible metal–organic frameworks with intrinsic thermal management. Nature, 2015

5:  DOI: 10.1038/nature15732

KEYWORDS: Self-sufficient UUV, Methane Powered UUV, Energy Harvesting UUV, UUV Methane Collection For UUV, Methane Conversion For UUV 

CONTACT(S): 

Mr. John Waterston 

john.waterston@darpa.mil 

Mr. Eric Weill 

TECHNOLOGY AREA(S): Info Systems 

OBJECTIVE: Develop the sensor hardware design and data processing and control algorithms necessary for an unmanned aircraft to maneuver to within proximity of and station keep with respect to a non-cooperative aircraft through visual references only. 

DESCRIPTION: There is a compelling DoD need for a lightweight, affordable, all-passive optical method for an unmanned aircraft to determine its relative position and maneuver with respect to a non-cooperative aircraft. Current solutions for an unmanned aircraft to operate and collaborate in close proximity to another aircraft requires precise state data to be communicated between aircraft. This requires them to carry inter-ship communications equipment and be designed to a common standard in a rigid, monolithic architecture. There may be applications, such as formation flying for advance remote sensing and airborne recovery of unmanned aircraft by a manned platform, where it is necessary to fly in close proximity but without benefit of these data links and common interfaces. Having the freedom to formation fly non-cooperatively will enable arbitrary platforms to come together to perform a coordinated mission, even when they were not designed as part of a single, cooperative architecture. Passive optical (visible or infrared) sensors provide the opportunity to deliver this non-cooperative station keeping capability at a minimal size, weight, power, and cost compared with other possible solutions, such as a radar or ladar. Recent developments in lightweight optical sensors, computing hardware, and algorithms allow relative position information to be calculated real-time by a single aircraft with no cooperative exchange of state information. 

PHASE I: Proposing teams must demonstrate a clear understanding of unmanned air vehicle systems and the supporting technologies required for coordinated operations. Key design goals include balancing system capability, precision, and robustness with low mass and affordability. Critical technologies include affordable passive optic sensors, miniaturized avionics, modular components, and ranging and relative positioning algorithms. Develop the design, fabrication concepts, and test approach for an affordable aircraft-agnostic passive optical-only (visible or shortwave infrared) system capable of determining relative position and maneuvering with respect to another non-cooperative aircraft. At a minimum, the system should be able to track another aircraft in clear weather and be able to maintain position and safely maneuver within a 60-degree cone aft of the target aircraft. Relative position accuracy can be a function of range but sufficient to avoid unintentional collision and maintain a formation. Maximum range can be a function of lead vehicle size, but far enough to enable target intercepts and relative maneuvering. Using the above goals, develop a system design and identify the performance goals, technical feasibility, and innovative enabling technologies. The design should include a detailed Phase II development plan for the technology addressing cost, schedule, performance, risk reduction, and appropriate testing. The proposer should identify technology and hardware risk reduction demonstrations at the component and system level. Hardware risk reduction during Phase I is encouraged although not required. This capability should be applicable to Group 3+ UAVs and corresponding flight performance characteristics to enable relative maneuvering and station keeping with similar size and capability aircraft. Phase I deliverables will include briefing charts reviewing system level applications, a military transition strategy, a thorough system design concept, and a Phase II development plan. The Phase II development plan will include the design, fabrication, and test plan of any proposed hardware or software systems. 

PHASE II: Build and fabricate the subsystems components based on the design concept developed in Phase I. As components of the system are completed, testing should be performed for risk reduction purposes and design validation. Plan and conduct an approach for system-level testing. This process may include providing live, virtual, and/or constructive simulations to demonstrate the overall capability of the hardware system and robustness of the algorithms. By the end of Phase II, the results from simulations, ground testing, and applicable demonstrations should lead towards the design of a flight test campaign, which will prove the feasibility and robustness of the system for future uses in commercial and DoD/military applications. Implementation of flight testing is not required for Phase II, but proposers are welcome to include flight testing opportunities that are within scope and do not negatively distract from the research value of the STTR. Phase II deliverables will include design details of the fabricated subsystem components and test results from simulations, ground testing, and applicable demonstrations. 

PHASE III: Commercial application - Visual relative navigation has the potential to greatly reduce the complexity and cost of operating commercial UAVs within the National airspace. Historically, active sensors or networked formations have been used to ensure aircraft deconfliction. Potential mission enhancing applications which may be of interest the commercial sector are as follows: • Remote sensor data collection utilizing a lead manned aircraft with a coordinated network of UAVs (e.g. multi-static radar sensing, overlapping EO/IR) can efficiently cover large areas of land for both agricultural and real estate characterization • Lead aircraft navigation and positioning of UAV’s for the beginning of an autonomous persistent surveillance or communication relay mission (i.e. self-forming leader-follower UAV “convoys”) • Airborne recovery of UAV’s after a homeland security mission by a manned aircraft. Additional technology transition opportunities shall be identified along with the most likely path for transition from STTR research to an operational capability. Military application - The DARPA-sponsored program, Gremlins, is investigating the use of a completely passive visual navigation system for air vehicle recovery. If proven successful, visual relative navigation can be incorporated into this or several ongoing DoD programs. Additional technology transition opportunities shall be identified by the performer along with the most likely path for transition from STTR research to an operational capability. Swarming UAVs in a communications-denied environment and the use of a UAV to conduct non-cooperative air operations are some examples of how this capability may be used. 

REFERENCES: 

1: S. M. Oh, E. N. Johnson, "Relative motion estimation for vision-based formation flight using unscented kalman filter", AAIA Guidance Navigation and Control Conference and Exhibit, pp. 20-23, August 2007. http://soliton.ae.gatech.edu/people/ejohnson/AIAA-2007-6866-195.pdf

2:  M. Fosbry, J. L. Crassidis, "Relative navigation of air vehicles", Journal of Guidance Control and Dynamics, 2008. http://www.acsu.buffalo.edu/~johnc/rel_nav08.pdf

3:  L. Meier, P. Tanskanen, F. Fraundorfer, and M. Pollefeys, "The pixhawk open-source computer vision framework for mavs." 2011. http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-1-C22/13/2011/isprsarchives-XXXVIII-1-C22-13-2011.pdf

KEYWORDS: Visual Relative Navigation, Cooperative Air-to-air Maneuvering, Non-cooperative Air-to-air Station Keeping, Passive Optical Sensors, UAV Navigation 

CONTACT(S): 

Mr. Scott Wierzbanowski 

scott.wierzbanowski@darpa.mil 

Dr. Timothy Chung