Gaussian-Student's t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference

Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking (MTT) system, a new Gaussian-Student's t mixture distribution probability hypothesis density (PHD) robust filtering algorithm based on variational Bayesian inference (GST-vbPHD) is proposed. Firstly, since it can accurately describe the heavy-tailed characteristics of noise with outliers, Gaussian-Student's t mixture distribution is employed to model process noise and measurement noise respectively. Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability, leading hierarchical Gaussian distribution constructed by the Gaussian-Student's t mixture distribution suitable to model non-stationary noise. Finally, the approximate solutions including target weights, measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach. The simulation results show that, in the heavy-tailed noise environment, the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student's t distribution PHD filter. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.