Cognitive Active Sonar Tracking for Optimum Performance in Clutter

Abstract

In this paper, a "cognitive" active sonar tracking algorithm is described, and results of its application to data from the LCAS’15 sea trial are shown. A key factor in tracker performance is the scheme used for track initiation and termination. A very common track initiation algorithm (TIA) is the sliding M-of-N processor, however, the tuning of its parameters can be difficult. It is often heuristic and sub-optimum, in achieving both good tracking performance of true targets as well as controlling the false track rate (FTR) for a desired sonar Pd/Pfa operating point. This is of particular concern for clutter-rich reverberation-limited undersea acoustic environments, where the false-alarm rates are high. The algorithm utilizes available in-situ data to estimate the statistics of the encountered clutter, and then optimizes tracker performance to meet specified operational levels. The adaptive algorithm is shown to effectively control the false track rate. The algorithm has potential to cognitively self-tune its operations for optimum performance.