具有相关观测值的分散传感器网络性能分析

N. Gnanapandithan, B. Natarajan
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引用次数: 2

摘要

本文研究了分散传感器网络在相关加性高斯噪声存在下的性能。提出了一种并行遗传算法,在最小化误差概率的意义上同时优化融合规则和局部决策规则。我们的研究结果表明,无论相关程度如何,该算法收敛于类多数融合规则,并且在相关观测的情况下,局部决策规则在确定整个系统的性能方面起着关键作用。我们还表明,随着观测值之间相关性的增加,系统的性能会下降
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the Performance of Decentralized Sensor Network with Correlated Observations
In this paper, we study the performance of a decentralized sensor network in the presence of correlated additive Gaussian noise. We propose a parallel genetic algorithm approach to simultaneously optimize both the fusion rule and the local decision rules in the sense of minimizing the probability of error. Our results show that the algorithm converges to a majority-like fusion rule irrespective of the degree of correlation and that the local decision rules play a key role in determining the performance of the overall system in the case of correlated observations. We also show that the performance of the system degrades with increase in the correlation between the observations
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