计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 50-56.doi: 10.11896/j.issn.1002-137X.2019.05.007

• 网络与通信 • 上一篇    下一篇

基于压缩感知的两阶段多目标定位算法

李秀琴, 王天荆, 白光伟, 沈航   

  1. (南京工业大学计算机科学与技术学院 南京211816)
  • 收稿日期:2018-04-16 修回日期:2018-09-28 发布日期:2019-05-15
  • 作者简介:李秀琴(1994-),女,硕士生,主要研究方向为压缩感知、无线传感器网络定位;王天荆(1977-),女,博士,副教授,主要研究方向为无线传感器网络、无线认知网络等,E-mail:wangtianjing@njtech.edu.cn(通信作者);白光伟(1961-),男,博士,教授,博士生导师,CCF高级会员,主要研究方向为移动互联网、无线传感器网络、社交网络、多媒体网络服务质量等;沈 航(1984-),男,博士,讲师,硕士生导师,CCF会员,主要研究方向为无线网络编码、移动互联网、无线多媒体通信协议等。
  • 基金资助:
    国家自然科学基金青年科学基金项目(61501224,61502230),江苏省自然科学基金(BK20150960,BK2010548),江苏省普通高校自然科学研究项目(15KJB520015),江苏省研究生科研与实践创新计划项目(SJCX18_0339)资助。

Two-phase Multi-target Localization Algorithm Based on Compressed Sensing

LI Xiu-qin, WANG Tian-jing, BAI Guang-wei, SHEN Hang   

  1. (School of Computer Science and Technology,Nanjing Tech University,Nanjing 211816,China)
  • Received:2018-04-16 Revised:2018-09-28 Published:2019-05-15

摘要: 针对传感器网络中基于接收信号强度(Received Signal Strength,RSS)的多目标定位具有天然稀疏性的问题,提出了基于压缩感知的两阶段多目标定位算法,该算法将基于网格的多目标定位问题分解为粗定位和细定位两个阶段。粗定位阶段,根据序贯压缩感知原理确定最优观测次数,然后利用lp最优化问题重构出目标所在的初始候选网格;细定位阶段,由四分法不断划分候选网格,根据最小残差原则估计目标在候选网格中的确切位置。仿真结果表明,相较于传统的基于l1最优化的多目标定位算法,基于压缩感知的两阶段多目标定位算法在目标个数未知的场景下具有更优的定位性能,且明显减少了定位时间。

关键词: 多目标定位, 无线传感器网络, 稀疏重构, 序贯压缩感知, 压缩感知

Abstract: The RSS-based multi-target location has the natural property of the sparsity in wireless sensor networks.In this paper,a two-phase multi-target localization algorithm based on compressed sensing was proposed.This algorithm divides the grid-based target localization problem into two phases:coarse location phase and fine location phase.In the coarse location phase,the optimal number of measurements is determined according to the sequential compressedsen-sing,and then the locations of the initial candidate grids are reconstructed by lp optimization.In the fine location phase,all candidate grids are continually divided by quadripartition method,and the accurate locations of targets in the corresponding candidate grids are estimated by using the minimum residual principle.Compared with the traditional multi-target localization algorithm using l1 optimization,the simulation results show that the proposed localization algorithm has better localization performance when the number of targets is unknown.Meanwhile,the localization time is significantly reduced.

Key words: Compressed sensing, Multi-target location, Sequential compressed sensing, Sparse reconstruction, Wireless sensor networks

中图分类号: 

  • TP393
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