计算机科学 ›› 2018, Vol. 45 ›› Issue (9): 161-165.doi: 10.11896/j.issn.1002-137X.2018.09.026
杨思星, 郭艳, 李宁, 孙保明, 钱鹏
YANG Si-xing, GUO Yan, LI Ning, SUN Bao-ming, QIAN Peng
摘要: 文中提出一种基于数据融合的压缩感知多目标定位算法,该算法能够同时处理多种不同类型的定位数据。与传统算法相比,该算法以目标个数的稀疏性为基础,通过压缩感知技术来重构目标位置向量,从而大大减少了传感器的数目。算法分为数据预处理和数据融合定位两个阶段。在数据预处理阶段,将不同类型的数据转换到同一个数量级,使得各类型数据能被充分用于提高目标定位性能;在数据融合定位阶段,提出一种基于多测量向量的压缩感知重构算法来估计目标位置向量。仿真证明,相比于现有的压缩感知定位算法,所提算法具有更高的定位精度和更强的鲁棒性。
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[1]RODRÍGUEZ M D,FAVELA J,MARTÍNEZ E A,et al.Location-aware access to hospital information and services[J].IEEE Transactions on Information Technology in Biomedicine,2004,8(4):448-55. [2]RALLAPALLI S,QIU L,ZHANG Y,et al.Exploiting temporal stability and low-rank structure for localization in mobile networks[C]∥Sixteenth International Conference on Mobile Computing and Networking.ACM,2010:161-172. [3]CANDÈS E J.Compressive sampling[C]∥Proceedings of the International Congress of Mathematicians.2006:1433-1452. [4]MAECHLER P,FELBER N,KAESLIN H.Compressive sen-sing for WiFi-based passive bistatic radar[J].2012:1444-1448. [5]CEVHER,DUARTE V M F,BARANIUK R G.Distributed target localization via spatial sparsity[C]∥Signal Processing Conference,2008.European IEEE,2008:1-5. [6]LAGUNAS E,SHARMA S K,CHATZINOTAS S,et al.Compressive sensing based target counting and localization exploiting joint sparsity[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing.IEEE,2016:3231-3235. [7]LIU H,DARABI H,BANERJEE P,et al.Survey of Wireless Indoor Positioning Techniques and Systems[J].IEEE Transactions on Systems Man & Cybernetics Part C Applications & Reviews,2007,37(6):1067-1080. [8]QIAN P,GUO Y,LI N,et al.Multiple target localization and power estimation in wireless sensor networks using compressive sensing[C]∥International Conference on Wireless Communications & Signal Processing.IEEE,2015:1-5. [9]HE T,HUANG C,BLUM B M,et al.Range-free localization schemes for large scale sensor networks[C]∥International Conference on Mobile Computing and Networking(MOBICOM 2003).2003:81-95. [10]XIN K,CHENG P,CHEN J.Multi-target localization in wireless sensor networks:a compressive sampling-based approach[J].Wireless Communications & Mobile Computing,2013,15(5):801-811. [11]LIU L,YUAN S,LV W,et al.A Multiple Target Localization with Sparse Information in Wireless Sensor Networks[J].International Journal of Distributed Sensor Networks,2016,2016:1-10. [12]ZHANG B,CHENG X,ZHANG N,et al.Sparse target counting and localization in sensor networks based on compressive sen-sing[C]∥INFOCOM.IEEE Xplore,2011:2255-2263. [13]MISHALI M,ELDAR Y C.Reduce and Boost:Recovering Arbitrary Sets of Jointly Sparse Vectors[J].IEEE Transactions on Signal Processing,2008,56(10):4692-4702. [14]YOU Y,CHEN L,GU Y,et al.Retrieval of sparse solutions of multiple-measurement vectors via zero-point attracting projection[J].Signal Processing,2012,92(12):3075-3079. [15]CANDES E J,WAKIN M B,BOYD S.Enhancing sparsity by reweighted minimization[J].Journal of Fourier Analysis and Applications,2008,14(5):877-905. [16]FENG C,VALAEEE S,TAN Z.Multiple target localization using compressive sensing[C]∥IEEE Conference on Global Telecommunications.IEEE Press,2009:4356-4361. |
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