He, ShaomingShin, HyosangTsourdos, Antonios2021-03-082021-03-082021-03-03He S, Shin HS, Tsourdos A. (2020) Information-theoretic joint probabilistic data association filter. IEEE Transactions on Automatic Control, Volume 66, Issue 3, March 2021, pp. 1262-12690018-9286https://doi.org/10.1109/TAC.2020.2989766https://dspace.lib.cranfield.ac.uk/handle/1826/16454This article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets. The proposed information-theoretic joint probabilistic data association algorithm is obtained by the minimization of a weighted reverse Kullback–Leibler divergence to approximate the posterior Gaussian mixture probability density function. Theoretical analysis of mean performance and error covariance performance with ideal detection probability is presented to provide insights of the proposed approach. Extensive empirical simulations are undertaken to validate the performance of the proposed multitarget tracking algorithm.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Information-theoretic approachjoint probabilistic data associationmultiple target trackingArticle