联合时延-多普勒-角度的无源雷达目标定位凸优化算法

杨静; 刘成城; 黄洁; 李霞

doi:10.12263/DZXB.20221276

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联合时延-多普勒-角度的无源雷达目标定位凸优化算法

学术论文 | 更新时间：2025-12-11

- 联合时延-多普勒-角度的无源雷达目标定位凸优化算法
- Convex Solution for Target Localization in Passive MIMO Radar Using Delay, Doppler and Angle Measurements
- 电子学报 2024年52卷第6期页码：2091-2102
- 作者机构：
  
  1.中国人民解放军战略支援部队信息工程大学数据与目标工程学院,河南郑州 450001
  2.中国人民解放军32738部队,河南郑州 450001
  3.中国人民解放军78090部队,四川成都 610000
- 作者简介：
  
  [ "杨静女，1985年12月出生于河南省方城县.现为中国人民解放军战略支援部队信息工程大学博士研究生.研究方向为无源雷达信号处理. Email:yangjing1028@126.com" ]
  [ "刘成城男，1986年7月出生于江苏省东台市.现为中国人民解放军战略支援部队信息工程大学数据与目标工程学院副教授.研究方向为雷达信号处理. Email: luckylcc079@126.com" ]
  [ "黄洁女，1973年9月出生于河南省郑州市.现为中国人民解放军战略支援部队信息工程大学数据与目标工程学院教授.研究方向为目标信息获取与处理. Email: huangjie0922@126.com" ]
  [ "李霞 1986年3月出生于四川省资阳市.现为中国人民解放军78090部队工程师.研究方向为计算机网络安全.E-mail: 331941684@qq.com" ]
- 基金信息：
  
  国家自然科学基金(62071490);河南省自然科学优秀青年基金(212300410095)
- DOI：10.12263/DZXB.20221276
  中图分类号： TN958.97;
- 收稿：2022-11-08，
  
  修回：2023-05-08，
  
  纸质出版：2024-06-25
- 稿件说明：
移动端阅览
杨静,刘成城,黄洁,等.联合时延-多普勒-角度的无源雷达目标定位凸优化算法[J].电子学报,2024,52(06):2091-2102.

YANG Jing, LIU Cheng-cheng, HUANG Jie, et al.Convex Solution for Target Localization in Passive MIMO Radar Using Delay, Doppler and Angle Measurements[J].Acta Electronica Sinica, 2024, 52(06): 2091-2102.
杨静,刘成城,黄洁,等.联合时延-多普勒-角度的无源雷达目标定位凸优化算法[J].电子学报,2024,52(06):2091-2102. DOI：10.12263/DZXB.20221276

YANG Jing, LIU Cheng-cheng, HUANG Jie, et al.Convex Solution for Target Localization in Passive MIMO Radar Using Delay, Doppler and Angle Measurements[J].Acta Electronica Sinica, 2024, 52(06): 2091-2102. DOI：10.12263/DZXB.20221276

摘要

针对分布式多输入多输出雷达中联合时延-多普勒-角度测量的运动目标定位问题，提出了一种基于半正定松弛的凸优化定位算法，改善了测量误差较大时定位误差偏离克拉美罗界的阈值效应.首先，将定位问题表述为最大似然估计问题，通过引入辅助变量将定位问题转化为带约束的加权最小二乘优化问题，然后使用半正定松弛技术，转化为半正定规划凸问题，并采用内点法对该凸问题求解得到目标位置和速度估计.由于凸优化问题的局部最优解就是全局最优解，本文算法具有良好的全局收敛性.仿真结果表明，与现有算法相比，本文算法的定位误差逼近克拉美罗下界，在大测量误差水平下的定位精度和稳健性优于现有算法.

Abstract

A convex-optimum localization algorithm based on semidefinite relaxation is proposed for moving target localization from time delay

Doppler shift and angle of arrival measurements in distributed multiple-input multiple-output radar. This algorithm alleviates the threshold effect that the positioning error deviates from the Cramer-Rao lower bound (CRLB) when the measurement error is large. First

the localization problem is formulated as a maximum likelihood estimation problem

which is reformulated as a weighted least squares problem with constraints by introducing auxiliary variables and then a convex semidefinite programming (SDP) problem by performing semidefinite relaxation. The SDP problem is solved efficiently by using the interior-point method to obtain the target position and velocity estimates. Since the local optimal solution of the convex optimization problem is the global optimal solution

the proposed algorithm has good global convergence. Simulation results demonstrate that the proposed algorithm approaches the CRLB

and achieves higher localization accuracy and robustness than existing algorithms at relatively large measurement noise levels.

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references

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