Advanced Data Exploitation for High-Performance Feature-Aided Sonar Tracking

This proposal addresses the challenge of incorporating non-kinematic features into tactical active sonar tracking algorithms used in anti-submarine warfare. The central focus is the development of three general methods that use feature information (i.e., their statistical characterization) to improve both data association and filtering. The methods are general in that they apply to a wide variety of features and trackers. They will be implemented and tested in STR’s Multi-Stage Multi-Hypothesis Tracker (MS-MHT). Based on gains seen in other domains, these feature-aided tracking techniques are expected to improve multi-target tracking performance in the presence of heavy clutter and high ambient noise.   In addition, STR proposes to develop an information theoretic approach to assess the impact non-kinematic features have on the data association problem. The approach is applicable to all three methods and could potentially be used to dynamically make decisions about whether or not to integrate a feature into a tracking solution.