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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Adaptive fusion by reinforcement learning for distributed detectionsystems
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Adaptive fusion by reinforcement learning for distributed detectionsystems

机译：通过强化学习对分布式检测系统进行自适应融合

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摘要

Chair and Varshney (1986) have derived an optimal rule for fusing decisions based on the Bayeslan criterion. To implement the rule, the probability of detection PD and the probability of false alarm PF for each detector must be known, but this information is not always available in practice. An adaptive fusion model which estimates the PD and PF adaptively by a simple counting process is presented. Since reference signals are not given the decision of a local detector is arbitrated by the fused decision of all the other local detectors. Furthermore, the fused results of the other local decisions are classified as “reliable” and “unreliable”. Only reliable decisions are used to develop the rule. Analysis on classifying the fused decisions in term of reducing the estimation error is given, and simulation results which conform to our analysis are presented
机译：Chair and Varshney（1986）基于贝叶斯兰准则得出了融合决策的最优规则。要实施该规则，必须知道每个检测器的检测概率PD和错误警报PF的概率，但实际上该信息并非始终可用。提出了一种通过简单的计数过程自适应估计PD和PF的自适应融合模型。由于未给出参考信号，因此由所有其他本地检测器的融合决策来仲裁本地检测器的决策。此外，其他本地决策的融合结果被分类为“可靠”和“不可靠”。仅使用可靠的决策来制定规则。给出了在减少估计误差的基础上对融合决策进行分类的分析，并给出了符合我们的分析结果的仿真结果

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