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...
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