Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars

Adaptive Sensing Techniques for Dynamic Target Tracking and Detection with Applications to Synthetic Aperture Radars by Gregory Evan Newstadt Chair: Alfred O. Hero, III This thesis studies adaptive allocation of a limited set of sensing or computational resources in order to maximize some criteria, such as detection probability, estimation accuracy, or throughput, with specific application to inference with synthetic aperture radars (SAR). Sparse scenarios are considered where the interesting element is embedded in a much larger signal space. For example, in wide area surveillance using synthetic aperture radars, the goal is to localize and track moving vehicles over a large scene. In this application, resources may be constrained in two ways: (a) limited dwell time of the radar in any particular location; and (b) limited computational resources in order to have a real-time detection/tracking system. Policies are examined that adaptively distribute the constrained resources by using observed measurements to inform the allocation at subsequent stages. This thesis studies adaptive allocation policies in three main directions. First, a framework for adaptive search for sparse targets is proposed to simultaneously detect and track moving targets. Previous work is extended to include a