Data association for target tracking rooted in maximum‐likelihood values

A modified version of the probabilistic data association (PDA) is proposed for target tracking under measurement uncertainty conditions. This method uses the likelihood of each validated measurement, and selects the best k candidates to be integrated with the PDA. Different computer simulations with a single target under dense‐cluttered environment are presented. Comparison to the standard PDA shows a track loss reduction with the proposed method.