Computer Science ›› 2019, Vol. 46 ›› Issue (4): 118-122.doi: 10.11896/j.issn.1002-137X.2019.04.019

• Network & Communication • Previous Articles     Next Articles

Dictionary Refinement-based Localization Method Using Compressive Sensing inWireless Sensor Networks

WU Jian1, SUN Bao-ming2   

  1. Suzhou Institute of Trade & Commerce,Suzhou,Jiangsu 215009,China1
    No.91977 of PLA,Beijing 102249,China2
  • Received:2018-03-19 Online:2019-04-15 Published:2019-04-23

Abstract: Traditional Compressive Sensing (CS)-based localization methods divide physical space into a fixed grid and assume that all targets fall on the grid precisely,therefore formulating the localization problem into a sparse reconstruction problem.In fact,it is very difficult to find such a fix grid because of the randomness of targets.As a result,there always exists mismatch between the assumed and actual sparse dictionaries,deteriorating localization performance significantly.This paper addressed this problem and proposed a novel dictionary refinement-based localization method using CS.In this method,the true sparse dictionary is modeled as a parameterized dictionary which views grids as adjustable parameters.Based on the model,the sparse dictionary is gradually refined by dynamically adjusting the grid.Consequently,the localization problem is formulated into a joint parameter estimation and sparse reconstruction problem,and this problem is solved under variational Bayesian inference framework.Simulation results show thatthe proposed localization method is more efficient and robust compared with traditional CS-based methods.

Key words: Wireless sensor networks, Compressive sensing, Dictionary refinement, Variational bayesian inference

CLC Number: 

  • TN911.7
[1]LIU Y,HE Y,LI M,et al.Does wireless sensor network scale? A measurement study on GreenOrbs [J].IEEE Transactions on Parallel and Distributed Systems,2013,24(10):1983-1993.
[2]LIU Y,YANG Z,WANG X,et al.Location,localization,and localizability [J].Journal of Computer Science and Technology,2010,25(2):274-297.
[3]DONOHO D L.Compressed sensing [J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[4]CANDE J.Compressive sampling[C]∥International Congress of Mathematicians,Madrid,Spain,2006:1433-1452.
[5]CEVHER V,DUARTE M,BARANIUK R G.Distributed target localization via spatial sparsity[C]∥Proceedings of the European Signal Processing Conference (EUSIPCO).Lausanne,Switzerland,2008:25-29.
[6]ZHANG B,CHEN X,ZHANG N,et al.Sparse target counting and localization in sensor networks based on compressive Sen-sing[C]∥IEEE International Conference on Computer Communications (INFOCOM).Shanghai,China,2011:2255-2263.
[7]HE F H,YU Z J,LIU H T.Multiple target localization via compressed sensing in wireless sensor networks [J].Journal of Electronics & Information Technology,2012,34(3):716-721.(in Chinese) 何风行,余志军,刘海涛.基于压缩感知的无线传感器网络多目标定位算法 [J].电子与信息学报,2012,34(3):716-721.
[8]LI Y B,HUANG H,YE F,et al.Target localization via compressed sensing based on SVD [J].Journal of Central South University (Natural Science),2014,45(5):1516-1521.(in Chinese) 李一兵,黄辉,叶方,等.基于奇异值分解的压缩感知定位算法 [J].中南大学学报,2014,45(5):1516-1521.
[9]WU D,ARKHIPOV D I,ZHANG Y,et al.Online war driving by compressive sensing [J].IEEE Transactions on Mobile Computing,2015,14(11):2349-2362.
[10]LAGUNAS E,SHARMA S K,CHATZINOTAS S.Compres- sive sensing based target counting and localization exploiting joint sparsity[C]∥2016 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2016:3231-3235.
[11]SUN B,GUO Y,LI N,et al.TDL:two-dimensional localization for mobile targets using compressive sensing in wireless sensor networks [J].Computer Communications,2016,78:45-55.
[12]XUE B,ZHANG L,YU Y.Multi-target localization based on sparse Bayesian learning in wireless sensor networks [J].IEICE Transactions on Communications,2016,99(5):1093-1100.
[13]CHI Y,SCHARF L,PEZESHKI A,et al.Sensitivity to basis mismatch in compressed sensing [J].IEEE Transactions on Signal Processing,2011,59(5):2182-2195.
[14]TZIKAS D,LIKAS A,GALATSANOSN P.The variational approximation for Bayesian inference [J].IEEE Signal Processing Magazine,2008,25(6):131-146.
[15]BRANCH M,COLEMAN T,LI Y.A subspace,interior,and conjugate gradientmethod for large-scale bound-constrained mi-nimization problems [J].SIAM Journal on Scientific Computing,1999,21(8):1-23.
[16]CHEN S,DONOHO D,SAUNDERS M.Atomic decomposition by basis pursuit [J].SIAM Journal on Scientific Computing,1998,20(1):33-61.
[17]TROPP J,GILBERT A.Signal recovery from random measurements via orthogonal matching pursuit [J].IEEE Transactions on Information Theory,2007,53(12):4655-4666.
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