26 research outputs found

    Bayesian Sensor Resource Allocation

    No full text
    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 techniques ignore this interaction. In this paper we develop an information gathering strategy which maximises the expected gain of the outcome (ie engagement). For example, the objective could be a function of the terminal miss distance and target identity 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 measurements may adversely affect chance of success (due to alerting target for example) and the fact that later measurements may be more informative but also may be of little use since there may 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 alterna..

    Robot Localization and Navigation Based on Multilevel Information Fusion

    No full text
    corecore