Abstract:

The target detection and tracking problems when involved in high dense clutter are addressed. Specifically, we propose to solve the optimization and computation problems of maximum likelihoodprobabilistic data association (ML-PDA). The particle swarm optimization (PSO) algorithm to maximize the log likelihood ratio (LLR) is adopted. We propose to initialize the particles of PSO based on measurements, which improves the computation efficiency. Furthermore, we propose a scheme which allows implementing PSO in parallel on graphic processing unit (GPU). The efficiency of the proposed algorithm and the parallelized scheme are illustrated based on simulations.