An Information Theoretic Approach to Optimal Sensor Data Selection for State Estimation

In this paper we introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. In contrast to common approaches, where a certain metric — for example, the mean squared error between true and estimated state — is optimized during state estimation, in this work the optimality is defined in terms of reduction in uncertainty in the state estimation process. The main assumption is that state estimation becomes more reliable if the uncertainty and ambiguity in the state estimation process can be reduced. We use Shannon’s information theory to select the camera parameters that maximize mutual information, thus optimizing the information that the captured image conveys about the true state of the system. The technique explicitly takes into account the a priori probabilities governing the computation of the mutual information. Thus a sequential decision process can be formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the previous time step. We demonstrate the benefits of our approach in an object recognition application using an active pan/tilt/zoom camera. During the sequential decision process the camera looks to parts of the object that allow the most reliable discrimination between similar objects. We