Passive Localization of Multiple Sources Using Joint RSS and AOA Measurements in Spectrum Sharing System

作     者：Kang Li Yutao Jiao Yehui Song Jinghua Li Chao Yue Kang Li;Yutao Jiao;Yehui Song;Jinghua Li;Chao Yue

作者机构：College of Communication EngineeringArmy Engineering University of PLANanjing 210007China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第12期

页      面：65-80页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

基　　金：This work was supported by the National Natu-ral Science Foundation of China(No.U20B2038 No.61901520 No.61871398 and No.61931011) the Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province(No.BK20190030) and the National Key R&D Program of China under Grant 2018YFB1801103. 

主　　题：multiple sources localization passive lo-calization received signal strength(RSS) angle of ar-rival(AOA) measurements-source association 

摘      要：In spectrum sharing systems,locating mul-tiple radiation sources can efficiently find out the in-truders,which protects the shared spectrum from ma-licious jamming or other unauthorized ***-pared to single-source localization,simultaneously lo-cating multiple sources is more challenging in prac-tice since the association between measurement pa-rameters and source nodes are not known.More-over,the number of possible measurements-source as-sociations increases exponentially with the number of sensor nodes.It is crucial to discriminate which measurements correspond to the same source before localization.In this work,we propose a central-ized localization scheme to estimate the positions of multiple sources.Firstly,we develop two computa-tionally light methods to handle the unknown RSS-AOA measurements-source association problem.One method utilizes linear coordinate conversion to com-pute the minimum spatial Euclidean distance sum-mation of measurements.Another method exploits the long-short-term memory(LSTM)network to clas-sify the measurement sequences.Then,we propose a weighted least squares(WLS)approach to obtain the closed-form estimation of the positions by linearizing the non-convex localization problem.Numerical re-sults demonstrate that the proposed scheme could gain sufficient localization accuracy under adversarial sce-narios where the sources are in close proximity and the measurement noise is strong.