The Multiple Hypotheses Tracking (MHT) algorithm has been shown to have the best tracking performance among existing multi-target tracking algorithms using real world sensors with probability of detection less than unity and in the presence of false alarms. The improved performance of the Multiple Hypotheses Tracking comes at the cost of signicantly higher computational complexity. Most Multiple Hypotheses Tracking implementations only form the best global hypothesis. This paper compares the Linear Multitarget Integrated Track Splitting (LMITS) tracking algorithm with the Multiple Hypotheses Tracking algorithm. LMITS has a simpler structure than Multiple Hypotheses Tracking as it decouples local hypotheses and avoids the measurement to multi-track allocation entirely. The number of LMITS hypotheses equals the sum of the number of local hypotheses added to the number of initiation hypotheses. Thus LMITS can retain a deeper hypotheses subtree which can result in better performance. We compare tracking performances of LMITS and MHT algorithms using simulated data for multiple maneuvering targets in heavy and non-uniform clutter.
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.