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 an iterative procedure for time-recursive multitarget/multisensor tracking based on 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, whereas the target-associated sensor outputs constitute the complete data. Target updates at each time use an EM-based approach that calculates the maximum a posteriori (MAP) estimate of the target states, under the assumption of appropriate motion models, based on the outputs of disparate sensors. The approach uses a Markov random field (MRF) model of the associations between observations and targets and allows for estimation of joint association probabilities without explicit enumeration. The advantage of this EM-based approach is that it provides a computationally efficient means for approaching the performance offered by theoretically optimum approaches that use explicit enumeration of the joint association probabilities. We provide selected results illustrating the performance/complexity characteristics of this EM-based approach compared with competing schemes.
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering