Labeled Random Finite Sets in Multi-target Track-Before-Detect

Abstract

In this paper we address the problem of tracking multiple targets based on raw measurements by means of Particle filtering. Bayesian multitarget tracking, in the Random Finite Set framework, propagates the multitarget posterior density recursively in time. Sequential Monte Carlo (SMC) approximations of the optimal filter are computationally expensive and lead to high-variance estimates as the number of targets increases. We propose a novel, efficient and reliable Labeled RFS based tracking algorithms suitable for, among others, the TBD surveillance application. This algorithm uses the Interacting Population based MCMC-PF (IP-MCMC-PF), first introduced in [6], as the core engine of a Multiple Cardinality Hypotheses Tracker (MCHT), where each cardinality is treated independently. The proposed multi-target filter is built upon the concept of labeled Random Finite Set (RFS) [40], [41], and formally incorporates the propagation and estimation of track labels within the RFS filtering framework. Simulation analyses demonstrate that the proposed Multiple Cardinality Hypotheses Particle Filter (MCHPF) yields higher consistency, accuracy and reliability in multitarget tracking. I