Decentralized detection algorithm with fuzzy model and self-learning weights

摘要

Abstract: This paper studies a design method of decentralized signal detection system which consists of the adaptive fuzzied local detectors and a data fusion rule of self-learning the weights on-line. The local detectors for the inaccurate signal parameters are modeled by means of fuzzy sets. Such a model can be adapted to change of the inaccurate signal parameters. The data fusion center can learn itself the local decision weights on-line based on the optimal decision rules. The combination 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 signal detection with an unknown parameter of unknown distribution and non-random unknown parameter. !8