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In this paper, a novel approach is proposed for multi-target joint detection,tracking and classification based on the labeled random finite set andgeneralized Bayesian risk using Radar and ESM sensors. A new Bayesian risk isdefined for the labeled random finite set variables involving the costs ofmulti-target cardinality estimation (detection), state estimation (tracking)and classification. The inter-dependence of detection, tracking andclassification is then utilized with the minimum Bayesian risk. Furthermore,the conditional labeled multi-Bernoulli filter is developed to calculate theestimates and costs for different hypotheses and decisions of target classesusing attribute and dynamical measurements. Moreover, the performance isanalyzed. The effectiveness and superiority of the proposed approach areverified using numerical simulations.

Multi-target Joint Detection, Tracking and Classification Based on Generalized Bayesian Risk using Radar and ESM sensors