Robust Labeled Multi-Bernoulli Filter with Inaccurate Noise Covariances

In this paper, a robust labeled multi-Bernoulli (RLMB) filter for the multi-target tracking (MTT) scenarios with inaccurate and time-varying process and measurement noise covariances is proposed. The process noise covariance and measurement noise covariance are modeled as inverse Wishart (IW) distributions, respectively. The state together with the predicted error and measurement noise covariances are inferred based on the variational Bayesian (VB) inference. Moreover, a closed-form implementation of the proposed RLMB filter is given for linear Gaussian system and the predictive likelihood function is calculated by minimizing the Kullback-Leibler (KL) divergence by the VB lower bound. Simulation results illustrate that the proposed RLMB filter outperforms the existing LMB filter in the tracking performance.