余东平¹,
郭艳^1,,,
李宁¹,
刘杰²,
杨思星¹
1.陆军工程大学通信工程学院 ??南京 ??210007
2.武警部队 ??北京 ??100089
基金项目:国家自然科学基金(61871400, 61571463)，江苏省自然科学基金(BK20171401)

详细信息

作者简介:余东平：男，1989年生，博士生，研究方向为信号处理、无线传感器网络定位
郭艳：女，1971年生，教授，博士生导师，研究方向为信号处理、压缩感知以及波束形成
李宁：男，1967年生，副教授，研究方向为认知无线电、自组织网
杨思星：女，1992年生，博士生，研究方向为信号处理、无源目标定位

通讯作者:郭艳　guoyan_1029@sina.com

中图分类号:TN911.7

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出版历程

收稿日期:2018-04-11
修回日期:2018-11-01
网络出版日期:2018-11-09
刊出日期:2019-02-01

Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information

Dongping YU¹,
Yan GUO^1,,,
Ning LI¹,
Jie LIU²,
Sixing YANG¹
1. College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China
2. The Chinese Armed Police Force, Beijing 100089, China
Funds:The National Natural Science Foundation of China (61871400, 61571463), The Natural Science Foundation of Jiangsu Province (BK20171401)


摘要
摘要:无源被动定位是入侵者检测、环境监测以及智能交通等应用的关键问题之一。现有的无源被动定位方法可通过信道状态信息获取多个维度上的测量信息，但是现有方案未能充分挖掘多个信道上的频率分集以提高定位性能。该文提出一种基于多维测量信息的压缩感知多目标无源被动定位算法，在压缩感知框架下利用多维测量信息的频率分集提高定位精度和鲁棒性。根据鞍面模型建立无源字典，将多目标无源被动定位问题建模成多测量向量联合稀疏恢复问题，并利用多维稀疏贝叶斯学习算法估计目标位置向量。仿真结果表明，该算法能有效利用多维测量信息提高定位性能。
关键词:无源被动定位/
压缩感知/
多测量向量/
稀疏贝叶斯学习
Abstract:Device-free passive localization is a key issue of the intruder detection, environmental monitoring, and intelligent transportation. The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance. This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework. The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm. Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.
Key words:Device-free passive localization/
Compressive Sensing (CS)/
Multiple measurement vectors/
Sparse Bayesian Learning (SBL)



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