Robust distributed fusion with trajectory random finite sets

Abstract

This paper concentrates on the distributed Multi-sensor Multi-target Tracking (DMMT) in the trajectory random finite set (TRFS) filtering framework. The trajectory probability hypothesis density (TPHD) filter and the trajectory cardinality PHD (TCPHD) are performed locally in different sensors for DMMT. Our analysis shows that standard generalized covariance intersection (GCI) and weighted arithmetic average (WAA) fusion with the posterior multitrajectory densities of the TPHD and TCPHD filters rely heavily on the proper association of trajectories estimated from various sensor nodes. Then, inspired by the analysis, we develop two novel distributed fusion algorithms for the Gaussian mixture TPHD and TCPHD filters. First, we obtain the associated and unassociated trajectories at different sensors via clustering and Linear programming (LP). Second, the associated trajectories are fused via covariance intersection (CI) parallelly. Third, two principle cardinality consensus algorithms are proposed to fuse unassociated and CI fused trajectories. Moreover, two analytical expressions, which are crucial for fusion weight in CI fusion, are provided. Finally, numerical experiments demonstrate the efficacy of our proposed approaches in computational efficiency and accuracy, compared with the state-of-the-art solutions in challenging scenarios.