Particle PHD filter multiple target tracking in sonar image

Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state estimate-to-track data association technique. The two approaches are implemented and compared on both simulated sonar and real forward-looking sonar data obtained from an autonomous underwater vehicle (AUV) and demonstrate that the PHD filter with data association compares well with traditional approaches for multiple target tracking