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Improved Performance Properties of the CISPRT Algorithm for Distributed Sequential Detection
Signal Processing ( IF 4.4 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.sigpro.2020.107573
Kun Liu , Yajun Mei

Abstract In distributed sequential detection problems, local sensors observe raw local observations over time, and are allowed to communicate local information with their immediate neighborhood at each time step so that the sensors can work together to make a quick but accurate decision when testing binary hypotheses on the true raw sensor distributions. One interesting algorithm is the Consensus-Innovation Sequential Probability Ratio Test (CISPRT) algorithm proposed by Sahu and Kar (IEEE Trans. Signal Process., 2016). In this article, we present improved finite-sample properties on error probabilities and expected sample sizes of the CISPRT algorithm for Gaussian data in term of network connectivity, and more importantly, derive its sharp first-order asymptotic properties in the classical asymptotic regime when Type I and II error probabilities go to 0. The usefulness of our theoretical results are validated through numerical simulations.

中文翻译:

用于分布式顺序检测的 CISPRT 算法的改进性能特性

摘要 在分布式顺序检测问题中,局部传感器随时间观察原始局部观察,并允许在每个时间步与其直接邻域通信局部信息,以便传感器可以协同工作,在测试二元假设时做出快速但准确的决定。真正的原始传感器分布。一种有趣的算法是 Sahu 和 Kar 提出的共识创新顺序概率比测试 (CISPRT) 算法(IEEE Trans. Signal Process., 2016)。在本文中,我们在网络连通性方面提出了关于高斯数据的 CISPRT 算法的错误概率和预期样本大小的改进有限样本属性,更重要的是,
更新日期:2020-07-01
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