Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing
Distributed Sequential Detection for Gaussian Shift-in-Mean Hypothesis Testing
复制标题
高斯均值平移假设检验的分布式顺序检测
DOI：
10.1109/tsp.2015.2478737
复制
发表时间：
2014
影响因子：
5.4
通讯作者：
S. Kar
中科院分区：
文献类型：
--
作者：
Anit Kumar Sahu;S. Kar
研究了分布式多智能体网络中的序贯高斯均值漂移假设检验问题。提出了一种基于共识+创新形式的分布式框架下的序贯概率比检验（SPRT）算法，在该算法中，智能体通过同时处理随时间顺序感知的最新观测（创新）和从相邻智能体（共识）获得的信息来更新其决策统计量.对于每个预先指定的I型和II型错误概率集合，导出局部决策参数，其确保算法实现期望的错误性能并且几乎肯定地（a.s.）每个网络代理。代理停止时间分布的尾部概率的大偏差指数，它示出渐近（在代理的数量或在高信号噪声比制度），这些指数与分布式算法的方法，最佳的集中式检测器。所提出的算法在每个网络代理的预期停止时间进行评估，并基准相对于最佳的集中式算法。所提出的算法的效率的意义上的预期停止时间的特点是在网络连通性。最后，模拟研究，说明和验证的分析结果。
This paper studies the problem of sequential Gaussian shift-in-mean hypothesis testing in a distributed multi-agent network. A sequential probability ratio test (SPRT) type algorithm in a distributed framework of the consensus+innovations form is proposed, in which the agents update their decision statistics by simultaneously processing latest observations (innovations) sensed sequentially over time and information obtained from neighboring agents (consensus). For each pre-specified set of type I and type II error probabilities, local decision parameters are derived which ensure that the algorithm achieves the desired error performance and terminates in finite time almost surely (a.s.) at each network agent. Large deviation exponents for the tail probabilities of the agent stopping time distributions are obtained and it is shown that asymptotically (in the number of agents or in the high signal-to-noise-ratio regime) these exponents associated with the distributed algorithm approach that of the optimal centralized detector. The expected stopping time for the proposed algorithm at each network agent is evaluated and is benchmarked with respect to the optimal centralized algorithm. The efficiency of the proposed algorithm in the sense of the expected stopping times is characterized in terms of network connectivity. Finally, simulation studies are presented which illustrate and verify the analytical findings.