Data association and tracking from distributed sensors using hidden Markov models and evidential reasoning

The problem of target tracking from distributed sensors in a cluttered environment is addressed. In `Data Association and Tracking Using HMMs and Dynamic Programming´, Proc. Conf. IEEE-ICASS 92, the authors introduced an approach which achieves target tracking and target motion analysis by using the hidden Markov models formalism and the Bayesian probabilities theory. This approach is theoretically valid in the single target case. A variant of this technique is introduced. It is valid in the multiple target case, with some restrictions in the case of close targets