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Comparison of track fusion rules and track association metrics

This paper presents numerical performance evaluation of various algorithms that have been developed for track-to-track fusion and association problems, through a long history of the distributed multiple target tracking algorithm development. We will use a general linear-Gaussian standard model both for the target state and the sensor observation models. Doing so, we can analytically evaluate any linear track fusion rule that produces a global target state estimate by fusing local target state estimates obtained by local sensor processing systems. In order to clearly compare performance of various track fusion rules, we will only utilize simple two-station (two-sensor) track association and fusion problems. Two typical situations, supplementary and complementary sensor scenarios, will be considered. Repeated track fusion with and without feedback will be examined in addition to simple one-time track fusion. For track-to-track association performance, we will compare the effects of using various track association metrics, proposed so far, in a simple one-time track association problem, through Monte Carlo methods.