Bayesian pattern matching technique for target acquisition

The following acquisition/selection problem is considered: a group of N tar gets is observed at time t(0) and one of them is designated (targeting info rmation). At some later time t(1) the target group is again observed by a m issile seeker. The targets are assumed to move as a group, as well as indiv idually, between observation times and so have a dependent motion model. Th e detection probability at t(1) is less than one, and so some of the target s may not be detected. It is also possible that some measurements received at t(1) may not originate from targets. The problem is to estimate the stat e of the designated target at time t(1), given the two sets of measurements , i.e., to recover the designated target. We employ a dependent target moti on model within a multiple hypothesis framework. The motion of the targets is modeled as the result of two effects: a bulk component, which is common to all targets, and an individual contribution, which is independent from t arget to target. A closed-form solution is derived for the linear-Gaussian special case, and simulation examples illustrating the technique are presen ted.