Bayesian Deghosting Algorithm for Multiple Target Tracking
Pavel Kulmon · 2020
This paper deals with bistatic track association in classical Frequency Modulation (FM) based Multi Static Primary Surveillance Radar (MSPSR). We formulate deghosting procedure as Bayesian inference of association matrix between bistatic tracks and targets as well as target positions. To do that, we formulate prior probability distribution for the association matrix and develop custom Monte Carlo Markov Chain (MCMC) sampler, which is necessary to solve such a hybrid inference problem. Using simulated data, we compare the performance of the proposed algorithm with two others and show its superior performance in such a setup. At the end of the paper, we also outline further research of the algorithm.