Integration of Bayes detection with target tracking

Existing detection systems generally are operated using a fixed threshold and optimized to the Neyman–Pearson criterion. An alternative is Bayes detection, in which the threshold varies according to the ratio of prior probabilities. In a recursive target tracker such as the probabilistic data association filter (PDAF), such priors are available in the form of a predicted location and associated covariance; however, the information is not at present made available to the detector. Put another way, in a standard detection/tracking implementation, information flows only one way: from detector to tracker. Here, we explore the idea of two-way information flow, in which the tracker instructs the detector where to look for a target, and the detector returns what it has found. More specifically, we show that the Bayesian detection threshold is lowered in the vicinity of the predicted measurement, and we explain the appropriate modification to the PDAF. The implementation is simple, and the performance is remarkably good.