Information-Theoretic Joint Probabilistic Data Association Filter

This article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets. The proposed algorithm is obtained by the minimization weighted reverse Kullback–Leibler divergence to approximate posterior Gaussian mixture probability density function. Theoretical analysis mean performance and error covariance with ideal detection presented provide insights approach. Extensive empirical simulations are undertaken validate multitarget algorithm.