具有模糊信息和自学习权重的分布式检测算法

Decentralized Detection Algorithm with Fuzzy information and Self-learning the Weights

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．

This paper studies a design method of decentralized signal detection system which consists of adaptive fuzzied local-detectors and a data fusion rule of on-line self-learning weights. The local--detectorS for inaccurate signal parameters are modeled by means of fuzzy sets which can be adapted to change of the inaccurate signal paraxneteres. The data fusion center Where the optimal decision rules are used as objective function can learn the local decision weights on-line. The robustness of the fuzzied local-detectors and the adaptability of the self-learned fusion rule make it true that the detection performance of the decentralized detection system is improved under uncertainty and this system can also process the decentralized signal detection with a unknown parameter of unknown distribution or non-random unknown parameter.