Computationally efficient tracking of multiple targets by probabilistic data association using neural networks

The joint probabilistic data association (JPDA) algorithm previously reported by T.E. Fortmann et al. (1983) for tracking multiple targets in the presence of clutter has the drawback that its complexity increases rapidly with the number of targets and returns. An approximation of the JPDA is suggested that solves this problem by using an analog computational network to solve the data association problem. The problem is viewed as that of optimizing a suitably chosen objective function. Simple neural-network structures for the approximate minimization of such functions have been proposed by other researchers. The analog network used here offers a significant degree of parallelism and thus can compute the association probabilities more rapidly. Computer simulations indicate the ability of the algorithm to track many targets simultaneously in the presence of moderate density clutter