Abstract:

The multi-target tracking methods based on δ-generalized labeled multi-Bernoulli (δ-GLMB) filter usually assume that the measurement noise covariance is known a priori. This is unrealistic for real applications, as it may be previously unknown or its value may be time-varying as the environment changes. To solve this problem, an adaptive δ-GLMB filtering algorithm based on variational Bayesian (VB) approximation is proposed. Based on the δ-GLMB filter, the proposed algorithm approximates joint posterior density of the measurement noise covariance and multitarget states by the mixture distribution of the products of inverse Wishart and Gaussian, and derives filtering iteration by the VB approximation. Simulation results indicate that the proposed algorithm has a strong robustness and could effectively estimate the measurement noise covariance and the number of targets as well as the corresponding multitarget states under the unknown measurement noise covariance scenario.