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Fusion-Aided Sensor Resource Management

Description:

TECHNOLOGY AREA(S): Sensors

OBJECTIVE: Navy Air Systems Command (NAVAIR) has a requirement to expand Research and Autonomy Innovation Development Environment and Repository (RAIDER) to support diverse Department of Defense (DoD) relevant missions. This SBIR should enable expansion of RAIDER capabilities by producing Future Airborne Capability Environment (FACE) or Open Mission Systems (OMS) compliant units of portability (UOPs) that provide Unmanned Aerial Systems (UAS) with resilient autonomous Sensor Resource Management (SRM). The SRM should include maneuvers and planning services as well as promote operational resilience. UOPs must be capable of managing unexpected circumstances occurring during a mission including unpredicted threats, unanticipated adversarial/non-combatant maneuvers, and overcoming losses of capability due to UAS and/or data link damage and malfunction.

DESCRIPTION: RAIDER UAS UOPs must be capable of satisfying operator provided mission objectives and rules of engagement by generating tactical decisions without further operator involvement. UOPs must utilize a principled approach to assure that UAS decisions are appropriate within objectives and time requirements. Operational resilience should be demonstrated by showing that the on-board planning with the UOPs is capable of: • Providing effective UAS autonomous collaborative communication supporting sensor fusion and tracking. UOPs should be capable of coordinating teams of 2-30 UAS to respond to maneuvers and threats from as many as 50 adversaries; maximizing allocation of sensors/platforms in a manned/unmanned teams (MUM-T) to optimize tracking performance and achieve mission goals. • Operating in denied environments in which communications and GPS are limited, and full connectivity between UAS and/or operator may not exist for periods throughout an engagement. UAS must overcome limits in communication and navigation. • Guaranteeing that a priori operator-provided rules of engagement are not violated. Rules of engagement may include geospatial, temporal, and behavioral constraints. • Supporting coordination between heterogeneous teams in which UAS may include different payloads, communications transceivers, and mobility characteristics.The DoD’s concept of employment of the above described UOP(s) is a fusion-aided SRM capability integrated into a distributed data fusion architecture.This approach will augment RAIDER to determine sensor resource allocations that improve track localization (where necessary) to form kill chains on identified targets of interest (TOIs).The fusion-aided SRM recommendations would be sent to a higher-level autonomy function where the data fusion recommendations could be weighed against and combined with recommendations or constraints from other onboard functions to determine the MUM-T resource reallocations.Problem Statement: Collaborative autonomous fusion UOPs are desired to generate a nearly-common operational picture (NCOP) amongst a group of UAS. In order for a set of MUM-T to develop high quality fused tracks in composable kill chains, an SRM capability is necessary to task sensors and platforms across a team to optimize track fusion.Technical goals specific to this proposal include developing a capability that:• Determines if a track in the fused scene meets required kill chain CEP/localization thresholds • Prioritizes sensor resource allocation based on target threat rankings obtained from mission priorities, Order of Battle, or a Threat Analysis module • Selects resources based on sensor type, availability, modes of operation, and proximity/range • Implements a Sensor Geometry Analysis algorithm that determines candidate sensor positions and number of sensors needed to improve fused track quality to meet minimum localization requirements • Collectively optimizes tasking and positioning across the all the platforms/sensors in the MUM-T to maintain the highest level of situational awareness and scene cohesion and localize high priority TOIs • Supports kill chain formation

PHASE I: Develop a prototype fusion-based capability to provide an SRM module with platform and sensor actions recommended to improve track quality in order to meet required target localization thresholds. Demonstrate UOP resilience in simulation-based experiments. Explain how the systems will be improved and demonstrated using RAIDER in Phase II.Methodology should be designed to address constraints on communication between UAS, i.e., a reduced subset of information can be shared. Information includes own-ship telemetry and sensor measurements or tracks or a combination of the two. Each UAS must be able to determine constraints on sharing information with other UAS in the distributed autonomous systems to support mission success. Such intelligent information sharing must consider the mission(s) objectives, time constraints, bandwidth constraints, mission constraints, and the information required to support the mission objectives.This topic is accepting Direct to Phase II proposals only. Proposers must provide documentation to substantiate that the scientific and technical merit and feasibility described in the Phase I section of the request for proposals has been met and describes the potential commercial applications. Documentation should include all relevant information including, but not limited to: technical reports, test data, prototype designs/models, and performance goals/results.• If you have references, include a reference list or works cited list as the last page of the feasibility documentation.This will count towards the page limit.• Work submitted within the feasibility documentation must have been substantially performed by the proposer and/or the principal investigator (PI). • If technology in the feasibility documentation is subject to IP, the proposer must have IP rights.• Include a one page summary on Commercialization Potential addressing the following:i. Does the company contain marketing expertise and, if not, how will that expertise be brought into the company? ii. Describe the potential for commercial (Government or private sector) application and the benefits expected to accrue from this commercialization. • DO NOT INCLUDE marketing material.Marketing material will NOT be evaluated.

PHASE II: Integrate autonomy UOPs into RAIDER-enabled UAS and conduct live flight demonstrations showing proof of concept for fusion-aided sensor resource management UOPs in relevant missions.Metrics shall be gathered from flight demonstration to show the completeness, accuracy, and timeliness of identifying, tracking, and localizing emitters.Collaborative autonomous fusion UOPs designed to address constraints on communication between UAS should be demonstrated. The intelligent information sharing must show consideration of the mission objectives, time constraints, bandwidth constraints, mission constraints, and the information required to support the mission objectives. Metrics should be gathered from demonstration to show fusion, information sharing effectiveness, communications effectiveness, and ability to thrive and complete desired mission in denied communications and GPS environments. Algorithms shall be demonstrated on operationally realistic simulated scenarios and modified/extended as necessary to address any challenges that arise during development and testing.

PHASE III: NAVAIR anticipates commercial applications for this technology.The commercial sector is expected to have thousands of drones operational within the next five years.Companies will undoubtedly find a wide variety of applications for drones as the industry continues to grow, and there will be a need to perform coordinated UAS functions such as managing deliveries of commercial goods, detecting and combating forest fires, and precision farming.Consider the commercial application of detecting and combating forest fires.Firefighting teams have limited resources with which to combat large forest fires.Therefore, it is critical that these teams allocate their resources to optimize their effectiveness in fighting these fires to minimize the fire damage.SRM algorithms deployed on a team of coordinated UAS could be used to optimize resource allocation to aid in the identification and localization of forest fire “hot spots” as well as hazardous areas to avoid.An SRM used to fight fires would be constructed similar to the approach described above.A firefighting SRM would1) identify affected regions that need improved SA, 2) prioritize these search regions, 3) identify available resources, 4) conduct a sensor geometry analysis using all combinations of available resources, and 5) optimize the sensor resource allocation to position the set of UAS to localize and identify prioritized fire “hot spots.”(Similar SRM applications can be applied to managing deliveries of commercial goods and precision farming.)Private sector commercial potential includes autonomous automobiles aircraft and trucks.

KEYWORDS: Autonomy, Manned-Unmanned Teaming, Data Fusion

References:

1. Defense Advanced Research Projects Agency. (2019, December 16). Collaborative Operations in Denied Environment. Retrieved from DARPA: https://www.darpa.mil/program/collaborative-operations-in-denied-environment 2. Open Group. (2019, December 16). Future Airborne Capability Environment. Retrieved from Open Group: https://www.opengroup.org/face 3. United Stats Air Force. (2017, September 27). Open Mission Systems. Retrieved from Virtual Distributed Laboratory: https://www.vdl.afrl.af.mil/programs/uci/oms.php

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