Compressive Sensing Based Wireless Localization in Indoor Scenarios

查看全文 作　　者：Cui Qimei Deng Jingang Zhang [1]Xuefei

机构地区：[1]Key Laboratory of Universal Wireless Communications, Beijing University of Posts and Telecommunications, Ministry ofEducation, Beijing 100876, P. R. China

出　　处：《China Communications》索引2012年第9卷第4期,共12页

基　　金：supported by the National Natural Science Foundation of China under Grant No.61001119;the Fund for Creative Research Groups of China under Grant No.61121001 摘　　要：The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location.CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy,which makes the CS-based solution very attractive for indoor positioning.However,CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence.In order to achieve a good recovery performance of sparse signals,CS-based solution needs to construct an efficient CS model.The model must satisfy the practical application requirements as well as following theoretical restrictions.In this paper,we propose two novel CS-based location solutions based on two different points of view:the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA).Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy. 关键词：无线定位可压缩室内场景 CS理论传感定位精度 CS算法稀疏性

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Compressive Sensing Based Wireless Localization in Indoor Scenarios

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