Multiple hypotheses tracking based distributed fusion using decorrelated pseudo measurement sequence

A joint probabilistic data association based algorithm for multi-target tracking in clutter using the distributed tracking architecture has been proposed recently. The algorithm uses the decorrelated state estimates or equivalent pseudo measurements. This paper extends the previous approach to the multi-target tracking problem in clutter with probability of detection less than unity using the track-oriented multiple hypotheses tracking framework. We present multiple hypotheses distributed tracking algorithms for track initialization, gating, hypothesis generation, track update, computation of track likelihood, formation of global hypothesis, and pruning using the pseudo measurement formulation.