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本文研究了一种由局部自适应模糊检测器和在线自学习融合算法所构成的分布式信号检测系统的设计方法由模糊集对不精确信号参数的局部检测器进行建模，该模糊模型可自适应不精确信号参数的变化,融合中心以最佳融合规则作为目标函数在线自学习局部判决的权重．局部模糊检测器的鲁律性和自学习融合算法的自适应性使该分布式检测系统在不确定环境下的检测性能得到提高也使该系统能够处理未知分布的未知参数以及非随机未知参数的分布式信号检测．
In this paper, we study a design method of distributed signal detection system composed of local adaptive fuzzy detector and on-line self-learning fusion algorithm. The local detector of inaccurate signal parameters is modeled by fuzzy set, Adapt to the change of inaccurate signal parameters, the fusion center takes the best fusion rule as the objective function to self-learn the weight of the local decision. The local fuzzy detector’s robustness and the self-learning algorithm’s self-adaptability make the distributed detection system improve the detection performance in uncertain environment and make the system handle unknown unknown parameters and non-random unknown parameters Distributed signal detection.