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In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density(PHD)flter 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 flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.
In Bayesian multi-target fltering, knowledge of measurement noise variance is very important. Signifcant mismatches in noise parameters will result in biased estimates. In this paper, a new particle flter for a probability hypothesis density (PHD) flter 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. other particle flters for this challenging class of PHD flters, the proposed method can adaptively learn the unknown and time-varying noise variances while fltering. Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.