Leveraging Machine Learning for Advanced Passive Sonar Tracking

PROJECTED CMMC LEVEL REQUIREMENT
Level 2 (Self)
TECHNOLOGY AREAS
None
MODERNIZATION PRIORITIES
Advanced Computing and Software
|
Trusted AI and Autonomy
KEYWORDS
Multi-sensor data fusion, operator workload reduction, advanced automation
OBJECTIVE
Develop advanced automation to detect, locate, classify, and correlate contacts across multiple sonar sensors and multiple display surfaces.
ITAR
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 section 3.5 of 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.
DESCRIPTION
Passive sonar systems employ a standardized signal processing pipeline to track, classify, and localize underwater contacts. This automated process, often referred to as "automation," begins after front-end processing generates visual displays for sonar operator analysis and automated processing. Existing algorithms that track energy signatures on these displays typically include Kalman filters, probabilistic multi-hypothesis trackers, and particle filters. However, these traditional tracking methods, as implemented in current operational systems, often fail to fully leverage the potential of modern machine learning techniques. This SBIR topic seeks to incorporate cutting-edge machine learning technologies into passive sonar processing to significantly improve tracking, classification, fusion, and localization of current anti-submarine warfare passive sonar systems. The specific threshold and goals for performance improvement are as indicated in the following table.
Targeted Improvement
Metric
Threshold
Objective
Tracking
Increase Hold Time Ratio
10%
20%
Tracking
Reduce Time to Detect
10&
20%
Classification
Increase Probability of Correct Classification
10%
15%
Classification
Reduce Probability of False Alerts
10%
15%
Track Fusion
Increase Probability of Correct Association
15%
20%
Localization
Reduce Area of Uncertainty
15%
20%
Work produced in Phase II may become classified. 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 ONR 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.
PHASE I
Develop algorithms that improve sonar automation for tracking, localization, classification, and multi-sensor fusion. The approach will reduce the burden of operators to maintain and promote tracks and be supported by theory.
PHASE II
Implement the proposed approach in a simulated environment (e.g., MATLAB) and demonstrate stated performance using government-provided data from a Navy sonar system. Important metrics will be, but not limited to, probability of correct association, hold time ratio, time to track, and probability of correct classification.
It is probable that the work under this effort will be classified under Phase II (see the Description section for details).
PHASE III DUAL USE APPLICATIONS
Support transition to Navy use.
This effort is anticipated to have dual-use applications in commercial surveillance systems with towed arrays or ISR uncrewed aerial vehicles. The performer shall identify possible non-Navy applications for their technology.
REFERENCES
Abraham. Douglas A. "Underwater Acoustic Signal Processing: Modeling, Detection, and Estimation." , Springer, 2019. https://www.google.com/search?q=Underwater+Acoustic+Signal+Processing%3A+Modeling%2C+Detection%2C+and+Estimation&rlz=1C1JZAP_enUS1043US1043&oq=Underwater+Acoustic+Signal+Processing%3A+Modeling%2C+Detection%2C+and+Estimation&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRiPAtIBBzg1NWowajSoAgCwAgE&sourceid=chrome&ie=UTF-8
Bell, Kristine L.; Corwin, Thomas L.; Stone, Lawrence D.; and Streit, Roy L. "Bayesian Multiple Target Track Second Edition." Artech House on Demand, 2014. https://us.artechhouse.com/Bayesian-Multiple-Target-Tracking-Second-Edition-P1802.aspx
Emami, P. et al. "Machine Learning Methods for Data Association in Multi-Object Tracking." ACM Computing Surveys, Vol. 53, Issue 4, Article 69, August 2020, pp. 1-34. https://arxiv.org/abs/1802.06897
Chong, C.Y. "An Overview of Machine Learning Methods for Multiple Target Tracking." 2021 IEEE 24th International Conference on Information Fusion (FUSION), Sun City, South Africa, 2021, pp. 1-9. https://ieeexplore.ieee.org/document/9627045