Joint optimization of detection and tracking in adaptive radar systems

Abstract

A promising line of research attempts to bridge the gap between a detector and a tracker by means of considering jointly optimal parameter settings for both of these subsystems. Along this fruitful path, this chapter focuses on the problem of detection threshold optimization in a tracker-aware manner so that a feedback from the tracker to the detector is established to maximize the overall system performance. Special emphasis is given to the optimization schemes based on two non-simulation performance prediction techniques for the probabilistic data association filter, namely, the modified Riccati equation (MRE) and the hybrid conditional averaging algorithm. The possible improvements are presented in non-maneuvering target tracking where a number of algorithmic and experimental evaluation gaps are identified and newly proposed methods are compared with the existing ones in a unified theoretical and experimental framework. Furthermore, for the MRE-based dynamic threshold optimization problem, a closed-form solution is proposed. This solution brings a theoretical lower bound on the operating signal-to-noise ratio concerning when the tracking system should be switched to the track-before-detect mode.