Finite resolution multitarget tracking
Darko Mušicki, Mark R. Morelande · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Target tracking algorithms have to operate in an environment of uncertain measurement origin, due to the presence of randomly detected target measurements as well as clutter measurements from unwanted random scatterers. A majority of Bayesian multi-target tracking algorithms suffer from computational complexity which is exponential in the number of tracks and the number of shared measurements. The Linear Multi-target (LM) tracking procedure is a Bayesian multi-target tracking approximation with complexity which is linear in the number of tracks and the number of shared measurements. It also has a much simpler structure than the "optimal" Bayesian multi-target tracking, with apparently negligible decrease in performance. A vast majority of target tracking algorithms have been developed with the assumption of infinite sensor resolution, where a measurement can have only one source. This assumption is not valid for real sensors, such as radars. This paper presents a multi-target tracking algorithm which removes this restriction. The procedure utilizes a simple structure of LM tracking procedure to obtain a LM Finite Resolution (LMfr) tracking procedure which is much simpler than the previously published efforts. Instead of calculating the probability of measurement merging for each combination of potentially merging targets, we evaluate only one merging hypotheses for each measurement and each track. A simulation study is presented which compares LMfr-IPDA with LM-IPDA and IPDA target tracking in a cluttered environment utilizing a finite resolution sensor with five crossing targets. The study concentrates on the false track discrimination performance and the track retention capabilities.