Multi-target Bayesian Tracking for Air Anti-Submarine Warfare (ASW) Systems

OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Sustainment; Trusted AI and Autonomy The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. OBJECTIVE: Develop a Bayesian tracker for Air Anti-Submarine Warfare (ASW) systems to improve tracking metrics when multiple targets (or target-like objects) are present in wide-area search missions. DESCRIPTION: Future Air ASW missions will cover larger areas than currently fielded systems and must operate in scenarios with multiple targets and surface ships, including: finding a single submarine in regions of high surface ship traffic (fishing fleets and shipping lanes), detecting multiple adversary submarines that are closely spaced and following similar paths (wolf packs), or coordinating search activities with U.S. Navy surface ships and submarines. Improvements to track initiation and measurement association are necessary to maintain reliable system performance in these challenging scenarios. Currently fielded track-before-detect approaches assume that a single undetected object such as a submarine, surface ship, or persistent clutter object, may be in the field at any given time, and as a result, performance is degraded when multiple objects ought to be promoted to contact followers at once. In addition, single hypothesis tracking can lead to confusion for low Signal-to-Noise-Ratio (SNR) targets with a significant number of clutter detections inside typical association gates. In the difficult association situations described previously, where tracks may cross or converge, the need for innovative approaches to the association problem are even more necessary. Advances in Bayesian tracking techniques have been developed to address these problems, even in the presence of substantial stationary clutter. Successful approaches to the multi-target tracking problem will initiate contact followers when multiple targets (or target-like objects such as surface ships) are present in the field, and maintain track integrity when multiple tracks are temporally and spatially close (within the area of uncertainty of a single measurement). Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations. Reference: National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. 2004.20 et seq. (1993). https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004 PHASE I: Design and develop an approach which reduces track initiation time by 30% when multiple objects are detected near the beginning of a mission which currently takes up to 10 pings. Show the proposed approach reduces false associations by 25% versus nearest-neighbor association when multiple targets are within the area of uncertainty of a single measurement. The Phase I effort will include prototype plans to be developed under Phase II. PHASE II: Develop prototype multi-target tracking software. Demonstrate that the software prototype reduces track initiation time by 50% and reduces false associations by 33% in at-sea data. Work in Phase II may become classified. Please see note in Description paragraph. PHASE III DUAL USE APPLICATIONS: Integrate the algorithm into the fleet-approved system for the Aircraft. The technology could be used in any field where Geo tracking is necessary. These technologies include Maritime tracking, Aircraft tracking, or Surface traffic tracking. REFERENCES: Vo, B. N., & Ma, W. K. (2006). The Gaussian mixture probability hypothesis density filter. IEEE Transactions on signal processing, 54(11), 4091-4104. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1710358 Saucan, A. A., Chonavel, T., Sintes, C., & Le Caillec, J. M. (2015). CPHD-DOA tracking of multiple extended sonar targets in impulsive environments. IEEE Transactions on Signal Processing, 64(5), 1147-1160. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7339699 KEYWORDS: Bayesian; Anti-submarine Warfare; ASW; Tracker; Multiple Targets; Clutter; Probability