Abstract
Tracking multiple moving targets involves many issues such as the sensors’ limited field of view, and the unknown number of targets with unknown dynamics. This paper performs multi-target tracking and target number estimation using a Gaussian mixture probability hypothesis density (GM-PHD) filter. Mutual information is calculated by approximate computation in nonparametric methods and the network of sensing robots is controlled to detect the maximum number of targets by maximizing the mutual information. In addition, we propose the motion pattern learning method using multiple Gaussian Process (GP) models to enhance the multi-target tracking performance for various types of movement by accurately predicting future target states. Among the multiple motion patterns learned in advance, the most proper pattern is assigned by the maximum likelihood principle. The performance of the proposed algorithm is validated via simulation in terms of the accuracy of target number estimation, and the reliability of multi-target tracking.
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