Leveraging Machine Learning for Advanced Passive Sonar Tracking
AI Overview
This SBIR seeks machine learning solutions to enhance passive sonar tracking, classification, and localization for anti-submarine warfare systems. The effort aims to improve detection speed, tracking persistence, and target classification accuracy beyond current algorithmic capabilities.
This summary is AI-generated from the official solicitation.
Key Details
Official 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 me...
Change History
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Q4 was answered: Navy prefers sensor-agnostic solutions in Phase 1 applicable to submarine/surface ship sonar, fixed arrays, and sonobuoys rather than specific acoustic receivers.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
**Summary of Q&A Changes:** Added answer to Q1 clarifying that Phase I has no real-time or latency requirements; these will be addressed in Phase II. Updated answer to Q2 to clarify that both single-sensor performance improvement AND multi-sensor fusion are desired objectives (previously ambiguous). All other Q&As (Q3-Q7) remain unchanged.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
**Changes to Q&A Section:** Added 2 new questions: Q1 on real-time vs. delayed processing requirements, and Q2 on whether the objective is to replace existing trackers or perform ML-based fusion with existing single-sensor trackers. Previous Q&A items were renumbered accordingly (Q1-Q5 became Q3-Q7).
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Added 1 new Q&A clarifying eligibility: U.S.-based primes cannot use Australian subcontractors; all R&D work must be performed in the United States per DoW Section 1.4.d. All other Q&As renumbered accordingly; technical content unchanged.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Added 1 new Q&A (Q1) asking whether the topic addresses specific acoustic receivers (sonobuoy, LVA, etc.) or is sensor-agnostic, with no answer provided yet. Previous Q&As renumbered accordingly (Q2-Q4).
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Status changed from Pre-Release to Open
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Added answer to Q1 recommending Sonar Simulation Toolset (SST) for DoD agencies/contractors, with technical report link. No recommendation provided for non-DoD entities. Q2 and Q3 answers remain unchanged.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Added 1 new Q&A: Q1 requests Navy recommendations for open source acoustic data generators for algorithm development (answer not yet provided). Previous Q&As renumbered as Q2-Q3 with no answer changes.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Q1 received a new answer clarifying that performance baselines should be relative to operational systems (sonobuoys, submarine sonar, etc.) or state-of-the-art trackers (Bayesian, Kalman filter, particle filter) if operational algorithms are inaccessible.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Added Q1 asking for baseline performance levels for tracking, classification, fusion, and localization metrics referenced in the proposal guidelines.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Q1 answered: Government will not provide data for Phase I, but will distribute recorded data (element time series/display surfaces) in Phase II if awarded. Applicants encouraged to use their own recorded or simulated data.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Q&A about government data provision for Phase I algorithm development, including data availability and format specifications.
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Close Date changed from 2026-04-22 to 2026-06-03
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Open Date changed from 2026-03-25 to 2026-05-06
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Status changed from Removed to Pre-Release
Leveraging Machine Learning for Advanced Passive Sonar Tracking
Opportunity DON26BZ01-NV025 no longer available
Leveraging Machine Learning for Advanced Passive Sonar Tracking
New opportunity: Leveraging Machine Learning for Advanced Passive Sonar Tracking
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