ADAPTIVE BEAMSTEERING COGNITIVE RADAR WITH INTEGRATED SEARCH-AND-TRACK OF SWARM TARGETS

Abstract

Unlike traditional radar systems, cognitive radars are designed to employ a perception-action cycle to continuously adapt to their environment. Adaptive beamsteering cognitive radar (AB-CRr) systems seek to improve detection and tracking performance by formulating a beam placement strategy adapted to their environment. Rather than employing traditional raster scanning in a search-scene, AB-CRr builds a probabilistic model of the target environment that enables it to more efficiently employ its limited resources to locate and track targets. In this thesis, we investigate methods for adapting the AB-CRr framework to detect and track large target swarms. This is achieved by integrating the properties of correlated-motion swarms into both the radar tracking model and AB-CRr’s underlying dynamic probability model. The results demonstrate that AB-CRr is capable of adapting its beamsteering strategy to efficiently perform resource balancing between search and tracking applications, while taking advantage of group structure and intra-swarm target correlation to resist large swarms overloading available resources.