Threshold Optimization for Tracking a Nonmaneuvering Target

A promising line of research attempts to bridge the gap between radar detector and radar tracker by means of considering jointly optimal parameter settings for both of these subsystems. We consider the problem of detection threshold optimization in a tracker-aware manner so that a feedback from the tracker to the detector is formed to maximize the overall system performance. We explore the research space for tracker-aware detector threshold optimization schemes and compare various approaches in a theoretical and experimental framework. In particular, we consider the optimization schemes which rely on two nonsimulation performance prediction (NSPP) algorithms for the probabilistic data association filter (PDAF), namely, the modified Riccati equation (MRE) and hybrid conditional averaging (HYCA). The study identifies a number of algorithmic and experimental evaluation gaps in this space and we propose to fill these gaps. Simulation results on relevant tracking scenarios demonstrate and discuss the behavior of the existing and proposed methods in terms of steady-state and transient tracking performance.