无线传感器网络中基于自适应网格的多目标定位算法

王天荆; 李秀琴; 白光伟; 沈航

doi:10.11959/j.issn.1000-436x.2019129

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无线传感器网络中基于自适应网格的多目标定位算法

学术通信 | 更新时间：2024-06-05

- 无线传感器网络中基于自适应网格的多目标定位算法
- Multi-target localization algorithm based on adaptive grid in wireless sensor network
- 通信学报 2019年40卷第7期页码：197-207
- 作者机构：
  
  南京工业大学计算机科学与技术学院，江苏南京 211816
- 作者简介：
  
  [ "王天荆（1977- ），女，江苏扬州人，博士，南京工业大学副教授、硕士生导师，主要研究方向为信号处理、无线网络和最优化方法。" ]
  [ "李秀琴（1994- ），女，江苏南京人，南京工业大学硕士生，主要研究方向为无线传感器网络和最优化方法。" ]
  [ "白光伟（1961- ），男，陕西西安人，博士，南京工业大学教授、硕士生导师，主要研究方向为无线网络、数据隐私和网络编码。" ]
  [ "沈航（1984- ），男，江苏南京人，博士，南京工业大学副教授、硕士生导师，主要研究方向为无线网络、数据隐私和网络编码。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501224);国家自然科学基金资助项目(61502230);国家自然科学基金资助项目(61602235);国家自然科学基金资助项目(61802176);江苏省自然科学基金资助项目(BK20161007);江苏省自然科学基金资助项目(BK20150960);江苏省研究生科研与实践创新计划基金资助项目(SJCX18_0339)
- DOI：10.11959/j.issn.1000-436x.2019129
  中图分类号： TP393
- 网络首发：2019-07，
  
  纸质出版：2019-07-25
- 稿件说明：
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王天荆, 李秀琴, 白光伟, 等. 无线传感器网络中基于自适应网格的多目标定位算法[J]. 通信学报, 2019,40(7):197-207.

Tianjing WANG, Xiuqin LI, Guangwei BAI, et al. Multi-target localization algorithm based on adaptive grid in wireless sensor network[J]. Journal on Communications, 2019, 40(7): 197-207.
王天荆, 李秀琴, 白光伟, 等. 无线传感器网络中基于自适应网格的多目标定位算法[J]. 通信学报, 2019,40(7):197-207. DOI： 10.11959/j.issn.1000-436x.2019129.

Tianjing WANG, Xiuqin LI, Guangwei BAI, et al. Multi-target localization algorithm based on adaptive grid in wireless sensor network[J]. Journal on Communications, 2019, 40(7): 197-207. DOI： 10.11959/j.issn.1000-436x.2019129.

摘要

针对无线传感器网络中基于RSS的多目标定位具有天然稀疏性的问题，提出了基于自适应网格的多目标定位算法，将多目标定位问题分解为大尺度网格定位和自适应网格定位2个阶段。大尺度网格定位阶段根据序贯压缩感知原理确定最优观测次数，再利用lp (0＆lt; p＆lt;1)最优化重构出存在目标的初始网格；自适应网格定位阶段根据压缩感知原理自适应划分初始网格，再利用lp最优化重构出目标的精确位置。仿真结果表明，相较于传统的基于压缩感知的多目标定位算法，所提算法在目标个数未知的场景下具有更高的定位精度和更低的定位时延，且更适合大规模无线传感器网络的多目标定位问题。

Abstract

The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed

which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase

the optimal number of measurements was determined due to the sequential compressed sensing theory

and then the locations of the initial candidate grids were reconstructed by applying lp (0＆lt; p＆lt;1) optimization.In the adaptive grid-based localization phase

the initial candidate grids were adaptively partitioned according to the compressed sensing theory

and then the locations of the targets were precisely estimated by applying lpoptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing

the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore

it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks.

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references

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