Dynamic factorization based multi-target Bayesian filter for multi-target detection and tracking

Abstract

This paper considers the problem of simultaneously detecting and tracking multiple targets based on the unthres-holed, track-before-detect style measurement model. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. [1] is the pioneer addressing this problem. However, the application of this work is largely restricted by its independence assumption which only holds when targets are well separated. This paper is committed to generalize this method to accommodate the arbitrary placement of targets. To this end, we propose a dynamic factorization based multitarget Bayesian filter which utilizes independence between targets whenever possible, while considers target estimation jointly when target states exhibit correlation. A novel sequential Monte Carlo implementation for the proposed multi-target Bayesian filter is also presented. Simulation results for a scenario with two crossing targets show the superior performance of the proposed filter.