Decentralized detection algorithm with fuzzy model and self-learning weights

Yuan Liu; Wanhai Yang; Ningzhou Cui; Weixing Xie

doi:10.1117/12.327114

17 July 1998 Decentralized detection algorithm with fuzzy model and self-learning weights

Yuan Liu; Wanhai Yang; Ningzhou Cui; Weixing Xie;

Author Affiliations +

Proceedings Volume 3374, Signal Processing, Sensor Fusion, and Target Recognition VII; (1998) https://doi.org/10.1117/12.327114
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Yuan Liu, Wanhai Yang, Ningzhou Cui, Weixing Xie, "Decentralized detection algorithm with fuzzy model and self-learning weights", Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327114; https://doi.org/10.1117/12.327114

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