基于稀疏恢复的雷达信号处理研究综述

全英汇; 吴耀君; 段丽宁; 徐刚; 薛敏; 刘智星; 邢孟道

doi:10.12000/JR23211

基于稀疏恢复的雷达信号处理研究综述

DOI: 10.12000/JR23211

全英汇^{1, 2, ,},
吴耀君^{1, 2},
段丽宁^{1, 2},
徐刚³,
薛敏¹,
刘智星^{1, 2},
邢孟道⁴

1.
西安电子科技大学电子工程学院西安 710071
2.
西安市先进遥感重点实验室西安 710071
3.
东南大学信息科学与工程学院毫米波全国重点实验室南京 210096
4.
西安电子科技大学前沿交叉研究院西安 710071

基金项目: 国家自然科学基金重点项目(62331019)，陕西省杰出青年科学基金(2021JC-23)，陕西省科技创新团队(2019TD-002)

详细信息

作者简介:
全英汇，博士，教授，主要研究方向为智能感知、敏捷雷达等

吴耀君，博士生，副研究员，主要研究方向为稀疏信号处理、捷变雷达抗干扰

段丽宁，硕士生，主要研究方向为捷变雷达抗干扰

徐　刚，教授，主要研究方向为雷达信号处理、雷达高分辨成像以及毫米波雷达成像等

薛　敏，博士生，主要研究方向为高分辨目标探测与稀疏信号处理等

刘智星，博士，主要研究方向为雷达信号处理及抗干扰

邢孟道，博士，教授，主要研究方向为SAR/ISAR成像、稀疏信号处理等

通讯作者:
全英汇 yhquan@mail.xidian.edu.cn

责任主编：刘一民 Corresponding Editor: LIU Yimin
中图分类号: TN951
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出版历程
- 收稿日期: 2023-11-02
- 修回日期: 2024-01-06
- 网络出版日期: 2024-01-11
- 刊出日期: 2024-02-28

A Review of Radar Signal Processing Based on Sparse Recovery

QUAN Yinghui^{1, 2
, ,},
WU Yaojun^{1, 2},
DUAN Lining^{1, 2},
XU Gang³,
XUE Min¹,
LIU Zhixing^{1, 2},
XING Mengdao⁴

1.
School of Electronic Engineering, Xidian University, Xi’an 710071, China
2.
Xi’an Key Laboratory of Advanced Remote Sensing, Xi’an 710071, China
3.
State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China
4.
Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62331019), The Shaanxi Provincial Science Fund for Distinguished Young Scholars (2021JC-23), The Science and Technology Innovation Team of Shaanxi Province (2019TD-002)

More Information

Corresponding author: QUAN Yinghui, yhquan@mail.xidian.edu.cn

摘要

摘要: 随着雷达目标探测需求的增加，基于压缩感知(CS)模型的稀疏恢复(SR)技术被广泛应用于雷达信号处理领域。该文首先对压缩感知的基本理论进行梳理；接着从场景稀疏以及稀疏观测两个角度介绍了雷达信号处理中的稀疏特性；然后基于稀疏特性，从空域处理、脉冲压缩、相参处理、雷达成像以及目标检测等角度概述了压缩感知技术在雷达信号处理中的应用。最后，对压缩感知技术在雷达信号处理中的应用进行了总结。
- 稀疏恢复(SR) /
- 压缩感知(CS) /
- 相参处理 /
- 目标检测 /
- 雷达成像
Abstract: With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.
- Sparse Recovery (SR) /
- Compressive Sensing (CS) /
- Coherent processing /
- Target detection /
- Radar imaging

HTML全文

图 1 压缩观测过程

Figure 1. The process of compressing observations

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图 2 频率捷变波形示意图

Figure 2. Schematic diagram of frequency agile signal

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图 3 频率稀疏编码波形示意图

Figure 3. Schematic diagram of frequency encoded modulated signal

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图 4 波形示意图

Figure 4. Schematic diagram of the waveform

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图 5 稀布直线阵列的几何结构

Figure 5. The geometry of the sparse linear array

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图 6 稀疏直线阵列的几何结构

Figure 6. The geometry of the thinned linear array

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图 7 均匀直线阵列

Figure 7. Uniform linear array

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图 8 稀布直线阵列

Figure 8. Sparse linear array

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图 9 稀疏直线阵列

Figure 9. Thinned linear array

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图 10 阵面阵元位置分布

Figure 10. Distribution of array element locations

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图 11 角度超分辨结果对比

Figure 11. Comparison of angular super-resolution results

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图 12 传统算法与OMP算法的脉冲压缩结果

Figure 12. Comparison of pulse compression results based on traditional algorithm and OMP algorithm

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图 13 PD雷达与脉间捷变雷达相位

Figure 13. Radar and pulse-to-pulse agile radar phase

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图 14 FFT算法与OMP算法

Figure 14. FFT algorithm and OMP algorithm

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图 15 目标所在距离单元的重构结果

Figure 15. Reconstruction results for the distance cell where the target is located

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图 16 传统SAR成像算法与压缩感知稀疏成像结果

Figure 16. Comparison of SAR imaging results based on traditional algorithm and CS algorithm

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图 17 稀疏孔径下的稀疏ISAR成像^[120]

Figure 17. Sparse ISAR imaging using sparse aperture^[120]

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图 18 ISAR成像性能分析^[121]

Figure 18. ISAR imaging performance analysis^[121]

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表 1 计算结果对比图

Table 1. Comparison chart of calculation results

理想角度位置	误差	OMP算法(°)
$ \begin{gathered} \left( {{\theta _1},{\phi _1}} \right) = \left( {{{\text{0}}^ \circ }, - {{\text{3}}^ \circ }} \right) \\ \left( {{\theta _{\text{2}}},{\phi _{\text{2}}}} \right) = \left( {{{\text{0}}^ \circ },{{\text{2}}^ \circ }} \right) \\ \end{gathered} $	$ {\hat \theta _1}{-}{\theta _1} $	0
	$ {\hat \phi _1}{{-}}{\phi _1} $	2
	$ {\hat \theta _2}{{-}}{\theta _2} $	0
	$ {\hat \phi _2}{{-}}{\phi _2} $	4

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表 2 成像性能分析

Table 2. ISAR imaging performance analysis

稀疏采样率	RMSE	CORR
0.1	0.95	0.32
0.2	0.87	0.49
0.3	0.78	0.63
0.4	0.68	0.74
0.5	0.58	0.82
0.6	0.49	0.88
0.7	0.40	0.92
0.8	0.31	0.95

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