Joint Tracking for Capturing and Classification Based on Joint Decision and Estimation

This paper presents an approach to joint tracking for capturing and classification (JTCC). Target tracking for capturing requires that the estimates be within a close neighborhood of the estimand rather than have a small average error, as for traditional tracking problems. Target classification determines the class of targets. Tracking for capturing is an estimation problem while classification is a decision problem, and they are highly coupled. So JTCC is a joint decision and estimation (JDE) problem. To solve this problem jointly, we first consider a generalized Bayes risk in a previously-proposed JDE framework. By minimizing this Bayes risk, we obtain the joint solution, and its estimation part simplifies to a generalized maximum a posteriori estimator. The JTCC approach adequately addresses the coupling between decision and estimation and the specifics of tracking for capturing. To evaluate the proposed algorithm jointly, we also give a joint performance measure: joint capturing and correct classification rate. Simulation results show that JTCC outperforms the decision-then-estimation, the separate decision-and-estimation, and the conditional joint decision-and-MMSE-estimation methods in joint performance measure.