Computer Science ›› 2018, Vol. 45 ›› Issue (9): 161-165.doi: 10.11896/j.issn.1002-137X.2018.09.026

• Network & Communication • Previous Articles     Next Articles

Compressive Sensing Multi-target Localization Algorithm Based on Data Fusion

YANG Si-xing, GUO Yan, LI Ning, SUN Bao-ming, QIAN Peng   

  1. Institute of Communication Engineering,PLA University of Science and Technology,Nanjing 210007,China
  • Received:2017-07-12 Online:2018-09-20 Published:2018-10-10

Abstract: This paper proposd a new compressive sensing localization algorithm based on data fusion,which can utilize all kinds of the localization data at the same time.The proposed theory is based on the sparsity of the target number and compressive sensing theory,and it can greatly reduce the quantity of sampling compared with the traditional localization algorithms.The new algorithm consists of data pre-processing and data fusion based localization.At the first step,different kinds of measurements are transferred into the form which has the same level.Then the technique of multiple measurement vectors is used to recover the target vector.Compared with other algorithms,the proposed algorithm holds better performance in localization accuracy and robustness.

Key words: Compressive sensing, Data fusion, Multiple measurement vectors, Multiple target localization, Wireless sensor networks

CLC Number: 

  • TP393
[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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