Comparison of Information-Theoretic Objective Functions for Decision Support in Sensor Systems

Information-driven sensor management aims at making optimal decisions regarding the sensor type, mode and configuration in view of the sensing objectives. In this paper, an approach is developed for computing two information-theoretic functions, expected discrimination gain and expected entropy reduction, to optimize target classification accuracy based on multiple and heterogeneous sensors fusion. The measurement process is modeled by means of Bayesian networks (BNs). The two objective functions utilize the BN models to represent the expected effectiveness of the sensors search sequence. New theoretic solutions are presented and implemented for computing the objective functions efficiently, based on the BN factorization of the underlying joint probability distributions. Dempster-Shafer fusion rule is embedded in the computations in order to account for the complementarity of multiple, heterogeneous sensor measurements. The efficiency of the two objective functions is demonstrated and compared using a landmine detection and classification application.