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Proceedings Paper

Decentralized detection algorithm with fuzzy model and self-learning weights
Author(s): Yuan Liu; Wanhai Yang; Ningzhou Cui; Weixing Xie
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Paper 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.

Paper Details

Date Published: 17 July 1998
PDF: 8 pages
Proc. SPIE 3374, Signal Processing, Sensor Fusion, and Target Recognition VII, (17 July 1998); doi: 10.1117/12.327114
Show Author Affiliations
Yuan Liu, Xidian Univ. (China)
Wanhai Yang, Xidian Univ. (China)
Ningzhou Cui, Xidian Univ. (China)
Weixing Xie, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 3374:
Signal Processing, Sensor Fusion, and Target Recognition VII
Ivan Kadar, Editor(s)

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