An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-12-12 DOI:10.1109/TSIPN.2023.3341407

Juan Augusto Maya;Leonardo Rey Vega;Andrea M. Tonello

{"title":"An Asymptotically Equivalent GLRT Test for Distributed Detection in Wireless Sensor Networks","authors":"Juan Augusto Maya;Leonardo Rey Vega;Andrea M. Tonello","doi":"10.1109/TSIPN.2023.3341407","DOIUrl":null,"url":null,"abstract":"In this article, we tackle the problem of distributed detection of a radio source emitting a signal. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively a statistic to decide if the source is present or absent. We model the radio source as a stochastic signal and work with spatially statistically dependent measurements. We consider the Generalized Likelihood Ratio Test (GLRT) approach to deal with an unknown multidimensional parameter from the model. We analytically characterize the asymptotic distribution of the statistic when the amount of sensor measurements tends to infinity. Moreover, as the GLRT is not amenable for distributed settings because of the spatial statistical dependence of the measurements, we study a GLRT-like test where the statistical dependence is completely discarded for building this test. Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistical dependence of the measurements has no impact on the detection performance in the asymptotic scenario. Furthermore, the GLRT-like algorithm has a low computational complexity and demands low communication resources, as compared to the GLRT.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"888-900"},"PeriodicalIF":3.0000,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10354397","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Signal and Information Processing over Networks","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10354397/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}

引用次数: 0

Abstract

In this article, we tackle the problem of distributed detection of a radio source emitting a signal. We consider that geographically distributed sensor nodes obtain energy measurements and compute cooperatively a statistic to decide if the source is present or absent. We model the radio source as a stochastic signal and work with spatially statistically dependent measurements. We consider the Generalized Likelihood Ratio Test (GLRT) approach to deal with an unknown multidimensional parameter from the model. We analytically characterize the asymptotic distribution of the statistic when the amount of sensor measurements tends to infinity. Moreover, as the GLRT is not amenable for distributed settings because of the spatial statistical dependence of the measurements, we study a GLRT-like test where the statistical dependence is completely discarded for building this test. Nevertheless, its asymptotic performance is proved to be identical to the original GLRT, showing that the statistical dependence of the measurements has no impact on the detection performance in the asymptotic scenario. Furthermore, the GLRT-like algorithm has a low computational complexity and demands low communication resources, as compared to the GLRT.

查看原文本刊更多论文

无线传感器网络分布式检测的渐近等效 GLRT 检验

在本文中，我们探讨了对发射信号的无线电源进行分布式检测的问题。我们认为，分布在不同地理位置的传感器节点会获取能量测量值，并通过合作计算统计量来判断信号源是否存在。我们将射电源建模为随机信号，并使用空间统计依赖性测量。我们考虑采用广义似然比检验 (GLRT) 方法来处理模型中的未知多维参数。我们分析了当传感器测量值趋于无穷大时统计量的渐近分布特征。此外，由于测量的空间统计依赖性，GLRT 不适用于分布式设置，因此我们研究了一种类似 GLRT 的检验方法，在建立该检验方法时完全摒弃了统计依赖性。尽管如此，其渐近性能与原始 GLRT 相同，这表明在渐近情况下，测量的统计依赖性对检测性能没有影响。此外，与 GLRT 相比，类 GLRT 算法的计算复杂度较低，所需的通信资源也较少。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.