Distributed Kalman Filter for Multitarget Tracking Systems With Coupled Measurements

Abstract

In multitarget tracking systems, it is usually assumed that each measurement is generated with respect to a single target. This is not always true for generating relative state measurements or cross-target information in a coupled fashion. This note is concerned with the problem of distributed filtering for multitarget tracking systems with coupled measurements. By representing the coupling features of the target states in the measurements as a directed graph, a modified Kalman consensus filter (KCF) is proposed for a target-dependent augmented system whose state vector consists of in-going neighborhood targets. To analyze the performance of the modified KCF in a directed graph, a sufficient condition is derived to guarantee the boundedness of the estimation errors in the mean square sense. Numerical studies are provided to verify the applicability of the KCF.