Toward coordinated sensor motion for classification: An example of intrusion detection using Bayes risk

In this paper we propose a framework for optimal coordinated sensor motion using the Bayes risk. For the purpose of illustration, we address an intrusion detection problem, which is cast as a binary hypothesis testing problem. We consider two distinct hypotheses or classes for moving targets. They are classified as threat or safe, depending on the future target trajectory entering or not entering a specified area of interest. The principal contribution of our work is a formal analysis, under various simplifying assumptions, of how Bayes risk can used to generate sensor motion control laws. We propose the use of the extended Kalman filter (EKF) state estimate and covariance as the summary statistic for the sensor observations. Thus the novelty of our approach lies in combining the classification and estimation problems formally, leading to an optimal coordinated sensor motion control algorithm.