Belief-Propagation-Based Resolvable Group Target Tracking With Unknown Detection Probability

Abstract

The problem of resolvable group target tracking (RGTT) with unknown detection probability is considered in this article. To adaptively match the detection probability used in the filtering process with the practical tracking environment, we propose a belief-propagation-based robust RGTT algorithm, named BP-R-RGTT. Specifically, the target state, existence variable, and detection variable are first used to construct the joint state vector of all potential targets. Then, given the measurements, the joint posterior probability density function is derived. Next, the recursion process of the proposed BP-R-RGTT is described in detail, where the marginal posterior distributions of variables are effectively obtained via the BP algorithm. Lastly, the Beta-Gaussian mixture-based implementations of the proposed BP-R-RGTT is proposed by using the GM and Beta distribution to represent the target state and detection probability, respectively. Simulation experiments are provided in single-sensor and multisensor RGTT scenarios to verify the superiority and robustness of the proposed BP-R-RGTT.