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19 May 2009 Toward mission-specific service utility estimation using analytic stochastic process models
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Abstract
Planning a mission to monitor, control or prevent activity requires postulation of subject behaviours, specification of goals, and the identification of suitable effects, candidate methods, information requirements, and effective infrastructure. In an operation that comprises many missions, it is desirable to base decisions to assign assets and computation time or communications bandwidth on the value of the result of doing so in a particular mission to the operation. We describe initial investigations of a holistic approach for judging the value of candidate sensing service designs by stochastic modeling of information delivery, knowledge building, synthesis of situational awareness, and the selection of actions and achievement of goals. Abstraction of physical and information transformations to interdependent stochastic state transition models enables calculation of probability distributions over uncertain futures using wellcharacterized approximations. This complements traditional Monte Carlo war gaming in which example futures are explored individually, by capturing probability distributions over loci of behaviours that show the importance and value of mission component designs. The overall model is driven by sensing processes that are constructed by abstracting from the physics of sensing to a stochastic model of the system's trajectories through sensing modes. This is formulated by analysing probabilistic projections of subject behaviours against functions which describe the quality of information delivered by the sensing service. This enables energy consumption predictions, and when composed into a mission model, supports calculation of situational awareness formulation and command satisfaction timing probabilities. These outcome probabilities then support calculation of relative utility and value.
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David J. Thornley, Robert J. Young, and James P. Richardson "Toward mission-specific service utility estimation using analytic stochastic process models", Proc. SPIE 7352, Intelligent Sensing, Situation Management, Impact Assessment, and Cyber-Sensing, 73520H (19 May 2009); https://doi.org/10.1117/12.818592
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