多传感器分布式检测综述

综述了多传感器分布式检测研究的发展、现状和最新进展。首先讨论了一些经典问题基于Neyman-Pearson和Bayes准则的分布式检测系统的优化，这包括局部判决的优化、融合准则的优化和全局优化。主要考虑了并行和串行结构，并且假设各传感器观测统计独立。给出了基于似然比检验(LikelihoodRatioTest,LRT)的最优准则。最后进一步讨论了分布式检测中的一些前沿方向渐近最优检测，非参数检测和CFAR检测以及序贯检测。

Survey of Distributed Detection With Multisensor

This paper summarizes the history, current status and recent progress of distributed detection with multisensor. At first, some classic issues are discussed, such as the optimization of distributed detection system based on Neymann-Pearson and Bayes criterion. It involves optimization of local decision and fusion rule and global optimization. The parallel and tandem structures are mainly considered. In addition, all the local observation in sensors are assumed to be independent. Some optimal rules based on likelihood ratio test (LRT) are given. At last, some advanced topics about distributed detection are discussed, which involves asymptotically optimal detection, nonparametric detection and CFAR detection and sequential detection.