多传感器分布式CFAR检测中Neyman-Pearson意义上的最优性

Guan Jian, Meng Xiang-wei, Peng Ying-ning, He You
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引用次数: 6

摘要

讨论了多传感器分布式CFAR检测中Neyman-Pearson (NP)意义上的最优性。现有的基于NP意义的分布式CFAR检测优化分析大多是在二元局部决策和局部处理器间无通信的限制下进行的。我们发现在这个限制下,NP意义上的真正优化是无法实现的。通过融合局部检验统计量,利用似然比检验实现真正的最优NP检验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The optimality in Neyman-Pearson sense in the distributed CFAR detection with multisensor
The optimality in Neyman-Pearson (NP) sense in distributed CFAR detection with multisensor is discussed. Most of the existing analysis of optimization of distributed CFAR detection in the NP sense is done under the limitation of binary local decision and no communication among local processors. We find that the real optimization in the NP sense can not be realized under this limitation. If local test statistics (LTS) are used and fused, the real optimal NP test could be implemented by likelihood ratio test (LRT).
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