Multi-Target Viterbi Data Association

Viterbi data association (VDA) is an approximate likelihood-based approach for solving the data association problem arising in target tracking. Here the VDA algorithm, previously developed for single target tracking in clutter, is extended to the multiple target case. The multi-target VDA (MT-VDA) formulation retains the main features of the VDA approach while generalizing the notion of a trellis to cover multiple target to measurement data association hypotheses. The cost metric for the problem is developed in terms of the multi-target association likelihood. Application of the Viterbi algorithm results in an efficient pruning strategy that can be represented graphically on a trellis. The MT-VDA algorithm is compared with a number of conventional zero-scan-back approaches including JPDA and is shown to provide significantly lower track loss, track swaps and tracking errors on a 1-D test scenario. The trellis merge depth is also investigated