计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 223-227.doi: 10.11896/j.issn.1002-137X.2018.01.039

• 网络与通信 • 上一篇    下一篇

基于动态格点的压缩感知目标计数和定位算法

杨思星,郭艳,刘杰,孙保明   

  1. 陆军工程大学通信工程学院 南京210007,陆军工程大学通信工程学院 南京210007,武警部队 北京100089,陆军工程大学通信工程学院 南京210007
  • 出版日期:2018-01-15 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金项目(61571463,61371124,61472445),江苏省自然科学基金(BK20171401)资助

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

摘要: 基于压缩感知技术的无线传感器网络定位,一般将定位区域划分为一定数目的网格并假定目标位于网格中心,然后通过求解一个1范数最小化问题来获得目标的位置。事实上,目标的随机性导致其很难位于网格中心,此时假定的变换基将无法稀疏表示位置信号,从而造成字典失配,使得定位精度下降。因此,提出一种基于动态格点的压缩感知定位算法。该算法能够自适应地调整格点的划分,使目标位于网格中心处。在求解过程中,该算法将复杂的优化问题转化成字典的更新和位置向量的求解两个部分的迭代来完成,同时实现了目标的计数和定位功能。仿真结果证明,与传统的压缩感知定位算法相比,所提算法在目标计数和定位方面都有更好的性能。

关键词: 动态格点,压缩感知,格点失配,多目标定位,目标计数

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.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!