A novel adaptive δ-generalised labelled multi-Bernoulli filter for multi-target tracking with heavy-tailed noise

Conventional delta-generalised labelled multi-Bernoulli filter (delta-GLMB) cannot deal with the problem of the heavy-tailed process noise and measurement noise. In order to solve this problem, an adaptive delta-GLMB approach based on minimising Kullback-Leibler Divergence (KLD) is proposed in this study. The inverse wishart and Student-t mixture is used to approximate the joint posterior distribution of process noise covariance and measurement noise covariance together with multi-target state, and the multi-target state and noise parameters are jointly estimated by minimising the KLD. The simulation results show that the proposed approach is robust to multi-target tracking for condition with heavy-tailed process noise covariance and measurement noise covariance. Accurate target number and target state estimation are obtained by effectively estimating the process noise covariance and measurement noise covariance.