An Information-Theoretic Approach to Sensor Resource Management for Situational Assessment

Toyon Research Corporation proposes to continue our research on Sensor Resource Management (SRM) designed to improve Situational Assessment (SA). Our approach is to cast SRM in the form of an optimization problem with an objective function featuring Shannon entropy. SA is defined as estimating entity states based on inferred relations among entities, therefore, reducing the entropy of the SA database will drive the system to detect, identify, classify, and track aggregates of vehicles. In Phase I, we have focused on the problem of identifying air defense systems to demonstrate how we can achieve a high degree of classification performance with relatively few sensor measurements. In Phase II, we will develop sophisticated SRM logic to better emulate the SA fusion process, improve the association and classification algorithms in the SA module, use a wider range of sensor operations, and use dynamic analysis of known entities to dictate the searching designed to identify new relations and detect tactically significant events. The resulting system will be demonstrated against significantly more complicated test problems featuring a variety of aggregate types and tactics occurring over multiple areas.