Adaptive Detection Threshold Optimization for Tracking in Clutter

Adaptive detection threshold optimization for improved downstream tracker performance with a Probabilistic Data Association Filter (PDAF) is investigated. Prior and posterior optimization algorithms which minimize the mean-square estimation error over the signal processor´s detection thresholds and which depend on observations up to the previous and current iteration, respectively, are given. These algorithms are suitable for real-time implementation. Simulation results are presented.