Joint Detection, Tracking, and Classification Using Finite Set Statistics and Generalized Bayesian Risk

In order to solve the target joint detection, tracking, and classification (JDTC) for multi-target, a recursive algorithm based on the labeled multi-Bernoulli (LMB) filter was presented under the framework of the conditional joint decision and estimation (JDE). A new generalized Bayesian risk is defined for the LMB variables involving the costs of target existence probability estimation, state estimation, and classification. Then the optimal solution is obtained according to the generalized Bayesian risk. The Gaussian mixture implementation of the proposed recursive JDTC algorithm is developed. The parameter selection for the new Bayesian risk is discussed, too. Simulation results show that the proposed method outperforms the traditional methods.

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