Particle filters for probability hypothesis density filter with the presence of unknown measurement noise covariance

In Bayesian multi-target filtering, knowledge of measurement noise variance is very important. Significant mismatches in noise parameters will result in biased estimates. In this paper, a new particle filter for a probability hypothesis density (PHD) filter handling unknown measurement noise variances is proposed. The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian (VB) methods. Moreover, the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions. Unlike other particle filters for this challenging class of PHD filters, the proposed method can adaptively learn the unknown and time-varying noise variances while filtering. Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.