Computer Science ›› 2018, Vol. 45 ›› Issue (1): 223-227.doi: 10.11896/j.issn.1002-137X.2018.01.039

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Dynamic Grid Based Sparse Target Counting and Localization Algorithm Using Compressive Sensing

YANG Si-xing, GUO Yan, LIU Jie and SUN Bao-ming   

  • Online:2018-01-15 Published:2018-11-13

Abstract: According to the localization algorithm based on Compressive Sensing (CS) in Wireless Sensor Network (WSN),the localization area is generally divided into a number of grids and the targets are located in the grid points.Then the locations of the targets can be obtained by 1-minimization algorithm.Practically,the assumption of the targets located in the grid points is almost impossible,which will make the location vector not sparse,and may lead to dictionary mismatch and cause error.As a result,a novel framework of localization approach based on dynamic grid was proposed.This approach can adaptively adjust the grid to make the targets exactly in the grid points.The proposed algorithm is solved by the iteration between the dictionary update and the obtaining of the location vector.At the same time,the algorithm can obtain the performance of both sparse target counting and localization.Simulation results show that the new proposed approach has advantages in both target counting and localization accuracy compared with the traditional CS-based algorithms.

Key words: Dynamic grid,Compressive sensing,Off-grid,Multiple target localization,Target counting

[1] RALLAPALLI S,QIU L,ZHANG Y,et al.Exploiting temporal stability and low-rank structure for localization in mobile networks[C]∥The Sixteenth International Conference on Mobile Computing and Networking.ACM,2010:161-172.
[2] PANWAR A,KUMAR S A.Localization schemes in wireless sensor networks[C]∥Proc 2nd International Conference on Advanced Computing & Communication Technologies.IEEE,2012:443-449.
[3] SHI G M,LIU D H,GAO D H,et al.Advances in theory and application of compressed sensing[J].Acta Electronica Sinica,2009,37(5):1070-1081.(in Chinese) 石光明,刘丹华,高大化,等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081.
[4] HE F H,YU Z J,LIU H T.Multiple target localization via compressed sensing in wireless sensor networks[J].Journal of Electronics and 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.
[5] CANDS E J.Compressive sampling[C]∥Proceedings of the International Congress of Mathematicians.2006:1433-1452.
[6] JI S,XUE Y,CARIN L.Bayesian compressive sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
[7] WU X P,LIU M Y.In-situ soil moisture sensing:measurement scheduling and estimation using compressive sensing[C]∥IEEE Conference on Information Processing in Sensor Networks (IPSN).2012:2012:1-12.
[8] FENG C,VALAEE S,TAN Z.Multiple target localization usingcompressive sensing[C]∥Global Telecommunications Confe-rence.IEEE,2009:1-6.
[9] CHEN F,AU W S A,VALAE E S,et al.Compressive Sensing Based Positioning Using RSS of WLAN Access Points[C]∥ Proceedings of the 29th Conference on Information Communications(INFOCOM’10).2010:14-19.
[10] ZHANG B,CHENG X,ZHANG N,et al.Sparse target counting and localization in sensor networks based on compressive sen-sing[C]∥INFOCOM,2011 .2011:2255-2263.
[11] PENG Q,YAN G,NING L,et al(1)An Improved Sensor Deployment Scheme for Multiple Target Localization using Compressive Sensing[C]∥International Conference on Electronics Information & Emergency Communication.2015:384-387.
[12] CANDES E J,ROMBERG J,TAO T.Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J].IEEE Transactions on Information Theo-ry,2006,2(2):489-509.
[13] 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.
[14] AUSTIN C D,ASH J N,MOSES R L.Dynamic dictionary algorithms for model order and parameter estimation[J].IEEE Transactions on Signal Processing,2013,61(20):5117-5130.
[15] LIU K,YU J J,HUANG Q H.Bi-object device-free localization based on compressive sensing[J].Journal of Electronics & Information Technology,2014,36(4):862-867.(in Chinese) 刘凯,余君君,黄青华.基于压缩感知的免携带设备双目标定位算法[J].电子与信息学报,2014,36(4):862-867.

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