Application of the EM algorithm for the multitarget/multisensor tracking problem

An important problem in surveillance and reconnaissance systems is the tracking of multiple moving targets in cluttered noise environments using outputs from a number of sensors possessing wide variations in individual characteristics and accuracies. A number of approaches have been proposed for this multitarget/multisensor tracking problem ranging from reasonably simple, though ad-hoc, schemes to fairly complex, but theoretically optimum, approaches. In this paper we describe a new iterative procedure for multitarget/multisensor tracking based upon use of the expectation-maximization (EM) algorithm. More specifically, we pose the multitarget/multisensor tracking problem as an incomplete data problem with the observable sensor outputs representing the incomplete data while the target-associated sensor outputs constitute the complete data. This formulation then allows a straightforward application of the EM algorithm which provides an iterative solution to the simultaneous maximum-likelihood (ML) and/or maximum a posteriori (MAP) estimate of the target states, under the assumption of appropriate motion models, based upon the outputs of disparate sensors. The advantage of this EM-based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum, but computationally infeasible, simultaneous ML estimator. We provide selected results illustrating the performance/complexity characteristics of this EM-based approach compared to competing schemes. This work supported in part by DAAH04-95-1-0103 (Laboratory for Information and Decision Systems, Massachusetts Institute of Technology).