Bayesian sensor resource allocation
Neil J. Gordon, M.D. Bedworth · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
Many tracking and guidance problems may be formulated as a terminating stochastic game in which the distribution of outcomes is affected by the intermediate actions. Traditional technique ignore this interaction. In this paper we develop an information gathering strategy which maximizes the expected gain of the outcome. For example, the objective could be a function of the terminal miss distance and target identify with penalties for missing a valid target or attacking a friendly one. Several trade-offs are addressed: the increased information available from taking more measurements, the fact that an increased number of measurement may adversely affect change of success and the fact that later measurements may be more informative but also may be of little use since there my not be enough time available for reaction to this extra information. The problem is formulated so that we are required to choose, under uncertainty, an alternative from a set of possible decisions. This set has a discrete uncertainty as to the number of measurements to be taken and a continuous uncertainty as to where and when the measurements should be taken. Preferences over consequences are modeled with a utility function. We propose to choose as optimal the alternative which maximizes expected utility. A simulation based approximation to the solution of this stochastic optimization problem is outlined. This relies on recent developments in dimensions swapping Markov Chain Monte Carlo (MCMC) techniques. The use of MCMC methodology allow us to explore the expected utility surface and thus select a measurement strategy. The resulting algorithm is demonstrated on a simple guidance problem.