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Track association using augmented state estimates

Track association has not received as much attention as track fusion in distributed multi-sensor multitarget tracking, especially for targets whose motion models involve process noise. One exception is an association metric that uses the cross-covariance of the track state estimates at a single time. For track fusion, it has been shown that the centralized state estimate can be obtained by fusion of augmented state estimates consisting of state estimates at multiple times. Association using augmented state estimates is even more natural because the association likelihood should consider the entire state trajectory of a track, and not just the estimates at the last time. Starting with a general association likelihood function, we show that augmented states allow exact evaluation of the track association likelihood. For problems involving Gaussian densities, the association metric is the standard Mahalanobis or chi-square metric with the single time state estimate replaced by the augmented state estimate. Simulations compare the performance of association using augmented state estimates of different lengths and the method using cross-covariances. Results demonstrate excellent performance for augmented state association even when the full augmented state is not used and filtered estimates instead of smoothed estimates are used.