Multiscan Multitarget Tracking in Clutter with Integrated Track Splitting Filter

A fully automatic tracking algorithm must be able to deal with an unknown number of targets, unknown target initiation and termination times, false measurements and possibly time-varying target trajectory behaviour. The approach presented in this paper follows the previously published integrated track splitting (ITS) framework which integrates a recursive calculation of the probability of target existence with multiscan trajectory estimation. This paper combines this framework with two multitarget tracking techniques. The first technique, joint multitarget tracking, enumerates and evaluates all feasible global measurement to track assignments resulting in a conditionally optimal but potentially computationally expensive technique. The second technique, linear multitarget (LM), achieves multitarget functionality by modulating clutter measurement density. LM is a suboptimal, but computationally very efficient technique. A simulation study is presented to show the effectiveness of this approach in the presence of nonuniform clutter when tracking targets in an environment where the targets perform violent manoeuvres.