An entropy-based approach to wide area surveillance

The use of measures from information theory to evaluate the expected utility of a set of candidate actions is a popular method for performing sensor resource management. Shannon entropy is a standard metric for information. Past researchers have shown1-5 that the discrete entropy formula can measure the quality of identification information on a target, while the continuous entropy formula can measure kinematic state information of a target. In both cases, choosing controls to minimize an objective function proportional to entropy will improve ones information about the target. However, minimizing entropy does not naturally promote detection of new targets or "wide area surveillance" (WAS). This paper outlines a way to use Shannon entropy to motivate sensors to track (partially) discovered targets and survey the search space to discover new targets simultaneously. Results from the algorithmic implementation of this method show WAS being favored when most targets in the search space are undiscovered, and tracking of discovered targets being favored when most targets are in track. The tradeoff between these two competing objectives is adjusted by the objective function automatically and dynamically.