Highest Probability Data Association for Active Sonar Tracking
Taek Lyul Song, Da Kim · 2006
We propose a new method of data association called highest probability data association (HPDA) combined with particle filtering and applied to active sonar tracking in clutter. The proposed HPDA method is a unification of probabilistic nearest neighbor and probabilistic strongest neighbor approaches. It evaluates the probabilities of one-to-one assignments of measurement-to-track. All of the measurements at the present sampling instance are lined up in the order of signal strength. The measurement with the highest probability is selected to be target-originated and the measurement is used for probabilistic weight update of particle filtering. The HPDA algorithm can be used in automatic target detection for track confirmation and estimation of the number of the targets. The proposed HPDA algorithm is easily extended to multi-target tracking problems. It can be used to avoid track coalescence phenomenon that prevails when several tracks move very close