Robust multi-target tracking under mismatches in both dynamic and measurement models

In multi-target tracking (MTT), it is hard to guarantee that the dynamic and measurement model mismatches are described by process and measurement noises with suitable covariance matrices (CMs). Meanwhile, model changes caused by target maneuvers and disturbance-corrupted measurements may coexist in a complex environment. Therefore, we propose the problem of MTT under mismatches in both dynamic and measurement models, where the uncertain state prediction error and measurement noise CMs are considered as random variables. Then, a robust generalized labeled multi-Bernoulli (RGLMB) filter framework is derived to recursively propagate the joint posterior density of the multi-target state and the random CMs. By modeling the random CMs as inverse Wishart distributions, on-line random CMs tuning and multi-target state estimation are unified via minimizing the Kullback–Leibler divergence. Simulation results show that the proposed RGLMB filter is robust to different target motion models and noisy radar measurements corrupted by step, sinusoidal and stochastic forms of bias.