Paper
3 September 1998 Bayesian sensor resource allocation
Neil J. Gordon, Mark D. Bedworth
Author Affiliations +
Abstract
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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Neil J. Gordon and Mark D. Bedworth "Bayesian sensor resource allocation", Proc. SPIE 3373, Signal and Data Processing of Small Targets 1998, (3 September 1998); https://doi.org/10.1117/12.324632
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Stochastic processes

Monte Carlo methods

Control systems

Sensors

Target detection

Detection and tracking algorithms

Statistical analysis

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