A modified PDAF based on a Bayesian detector
Peter Willett, Ruixin Niu, Yaakov Bar‐Shalom · 2000
Practical detection systems generally are operated using a fixed threshold, optimized to the Neyman-Pearson criterion. An alternative is Bayes detection, in which the threshold varies according to the ratio of prior probabilities. This prior information is available in a tracking situation, but appears little used. The effect here is of a depressed detection threshold near the predicted measurement. We explain the appropriate modification to the commonly used probabilistic data association and tracking filter (PDAF). The implementation is simple, and the performance is remarkably good, and a considerable advantage with respect to the fixed-threshold PDAF is observed.