Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000165-6.doi: 10.11896/jsjkx.211000165

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Intelligent Jammers Localization Scheme Under Sensor Sleep-Wakeup Mechanism

YANG Si-xing1,2, LI Ning1, GUO Yan1, YANG Yan-yu2   

  1. 1 Institute of Communication Engineering,Army Engineering University of PLA,Nanjing 210000,China
    2 The PLA Unit 94701,Anqing,Anhui 246000,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:YANG Si-xing,born in 1992,Ph.D.Her main research interests include Internet of things,wireless networks,and device-free target localization.
    LI Ning,born in 1965,master,associate professor.His main research interests include Ad hoc networks,digital beamforming and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61871400)and Natural Science Foundation of Jiangsu Province,China(BK20211227).

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Abstract: Intelligent jammer can change its transmitting power to improve the jamming effect adaptively with the developing artificial intelligence(AI) technique,making the traditional localization scheme out of work.Therefore,this paper investigates the block compressive sensing(BCS) based multi-jammer localization scheme under sensor wake-up mechanism.Firstly,the sensor nodes are periodically awakened to prolong the lifetime of the network and to collect more accurate localization information.Se-condly,this paper introduces the reference power to avoid the issue that the relationship between the distance and the varying power are unknown.Thirdly,we utilize the compressive sensing(CS) theory to build the localization issue as a BCS recovery problem.Finally,a novel Wake-VBEM algorithm under the variational Bayesian mean-expect is proposed by exploring the power variation law.Simulations show that the proposed method can simultaneously estimate the location of multi-jammers and prolong the lifetime of the network even the power of the jammer is unknown and varying.

Key words: Jammer localization, Wireless sensor networks, Sensor wakeup, Block compressive sensing, Variational Bayesian meanexpect

CLC Number: 

  • TN919
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