Multiple target tracking in Underwater Sonar Images using Particle-PHD filter

A multiple feature tracking algorithm for Sector Scan Sonar images is presented. The underlying framework that is employed is the Random Finite Sets (RFS) approach which is the natural representation of the multi-target states and observations. There has been a strong mathematical foundation laid to this approach using Finite Set Statistic(FISST). However, the propagation of the full multi-target posterior distribution using the optimal Bayesian approach is not yet practical due to computational hurdles. A practical alternative to the optimal Bayesian multi-target filter based on RFS is the probability hypothesis density (PHD) filter. PHD is a first order statistical moment of the full multi-target posterior distribution. This paper deals with feature detection and estimation techniques using the particle-PHD filter. The ability to track features in heavy clutter is demonstrated first using the simulated data and then with the real sector scan sonar data.