Multi-object Bayesian filter for jump Markov system under glint noise

Abstract

Abstract The probability hypothesis density filter under glint noise is efficient for multi-object tracking. However, this filter is inapplicable to tracking the multiple maneuvering objects in case of low detection probability. In order to track the multiple maneuvering objects under glint noise more efficiently, we propose a novel multi-object Bayesian (MOB) filter for jump Markov system under glint noise by applying jump Markov system models and variational Bayesian approach to the MOB filter and develop an implementation of this filter. The developed implementation uses the Student's t-distribution to depict the glint noise, uses the Gaussian-Gamma distribution to represent the predicted state distribution and updated state distribution of each target, and uses the variational Bayesian method to acquire the approximate state distribution of each target. A comparison of the proposed MOB filter with the existing filters demonstrates that the proposed MOB filter is better at tracking the maneuvering objects under glint noise than the existing filters.