PROJECT TITLE :

Multi-target tracking in clutter using a high pulse repetition frequency radar

ABSTRACT:

During this study, 2 algorithms of single-target tracking in muddle employing a high pulse repetition frequency radar are extended: the Gaussian mixture measurement likelihood-integrated track splitting (GMM-ITS) algorithm and the enhanced multiple models (MM) to multi-target tracking algorithm, that's, the GMM-joint ITS algorithm and the enhanced MM-joint probabilistic data association algorithm, respectively. Both algorithms are extended on the premise of the optimal Bayes approach that makes track clusters for determining the nearby tracks that share measurements by enumerating and evaluating all the feasible joint measurement allocations. In all cases, the track trajectory probability density function could be a Gaussian mixture, and each algorithms enable false track discrimination using the probability of target existence.