A Bayesian pattern matching technique for target acquisition

The following acquisition/selection problem is considered: a group of N targets is observed at time t0 and one of them is designated (targeting information). At some later time t1 the target group is again observed from a stand-oo missile. The targets are assumed to move as a group, as well as individually, between observation times and so have a dependent motion model. The detection probability at t1 is less than one and so not all targets may have been detected. There is also the possibility that not all measurements received at t1 originate from targets. The problem is to estimate the state of the designated target at time t1, given the two sets of measurements, ie to recover the designated target. In this paper we employ a dependent target motion model within a multiple hypothesis framework. The motion of the targets is modelled as the result of two eeects: a bulk component which is common to all targets and an individual contribution which is independent from target to target. A closed form solution is derived for the linear-Gaussian special case and a simulation example illustrating the technique is presented.