IAA-Net:一种实孔径扫描雷达迭代自适应角超分辨成像方法

毛德庆; 杨建宇; 杨明杰; 张永超; 张寅; 黄钰林

doi:10.12000/JR24063

IAA-Net:一种实孔径扫描雷达迭代自适应角超分辨成像方法

doi: 10.12000/JR24063

电子科技大学信息与通信工程学院成都 611731

基金项目: 国家自然科学基金(62301131)，博士后科学基金(BX20220055, 2022M720667)

详细信息

作者简介:
毛德庆，博士，主要研究方向为雷达信号处理等

杨建宇，教授，博士，主要研究方向为合成孔径雷达和统计信号处理等

杨明杰，硕士生，主要研究方向为机载雷达前视智能化超分辨成像等

张永超，副研究员，博士，主要研究方向为阵列信号处理和雷达应用中的逆问题等

张　寅，研究员，博士，主要研究方向为信号处理和雷达成像等

黄钰林，教授，博士，主要研究方向为雷达成像、检测与识别和机器学习等

通讯作者:
毛德庆 deqingmao@uestc.edu.cn

张寅 yinzhang@uestc.edu.cn

责任主编：朱岱寅 Corresponding Editor: ZHU Daiyin
中图分类号: TN957
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出版历程
- 收稿日期: 2024-04-09
- 修回日期: 2024-06-23

IAA-Net: An Iterative Adaptive Approach for Angular Super-resolution Imaging of Real Aperture Scanning Radar

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62301131), The China Postdoctoral Science Foundation (BX20220055, 2022M720667)

More Information

Corresponding author: MAO Deqing, deqingmao@uestc.edu.cn; ZHANG Yin, yinzhang@uestc.edu.cn

摘要

摘要: 实孔径雷达(RAR)通过天线扫描工作，以获取大范围探测区域内目标的观测信息。但是，由于雷达天线尺寸小，受天线衍射机理限制，与距离分辨率相比，其角分辨率通常较低。角超分辨处理方法，可利用天线方向图与目标散射间的卷积关系，通过求解卷积反演问题，以提高扫描雷达角分辨率。但是，由于测量矩阵的低秩特性，传统角超分辨处理方法，存在正则化参数选择难、迭代更新慢等问题，并且在低信噪比条件下，角超分辨处理性能明显下降。针对上述问题，该文提出了一种基于深度网络的迭代自适应实孔径扫描雷达角超分辨成像方法。首先，本文将实孔径扫描雷达的卷积反演问题转化为回波自相关矩阵反演求解问题，以改善求逆矩阵的病态性；其次，将可学习的修正矩阵引入到迭代自适应求解方法中，以实现迭代自适应求解方法与深度网络的结合；最后，通过迭代学习更新回波自相关矩阵，降低噪声对反演结果的影响，提高实孔径雷达的角分辨率。仿真及实测数据结果表明，所提方法可避免传统算法中的手动参数选择和迭代更新慢等问题。同时，由于深度网络的学习拟合能力，所提方法可在低信噪比条件下保持良好的角超分辨性能。
- 实孔径雷达(RAR) /
- 角超分辨重建 /
- 深度网络 /
- 迭代自适应 /
- 参数选择
Abstract: Real Aperture Radar (RAR) observes wide-scope target information by scanning its antenna. However, because of the limited antenna size, the angular resolution of RAR is much lower than the range resolution. Angular super-resolution methods can be applied to enhance the angular resolution of RAR by inverting the low-rank steering matrix based on the convolution relationship between the antenna pattern and target scatterings. Because of the low-rank characteristics of the antenna steering matrix, traditional angular super-resolution methods suffer from manual parameter selection and high computational complexity. In particular, these methods exhibit poor super-resolution angular resolution at low signal-to-noise ratios. To address these problems, an iterative adaptive approach for angular super-resolution imaging of scanning RAR is proposed by combining the traditional Iterative Adaptive Approach (IAA) with a deep network framework, namely IAA-Net. First, the angular super-resolution problem for RAR is transformed into an echo autocorrelation matrix inversion problem to mitigate the ill-posed condition of the inverse matrix. Second, a learnable repairing matrix is introduced into the IAA procedure to combine the IAA algorithm with the deep network framework. Finally, the echo autocorrelation matrix is updated via iterative learning to improve the angular resolution. Simulation and experimental results demonstrate that the proposed method avoids manual parameter selection and reduces computational complexity. The proposed method provides high angular resolution under a low signal-to-noise ratio because of the learning ability of the deep network.
- Real Aperture Radar (RAR) /
- Angular superresolution reconstruction /
- Deep network framework /
- Iterative adaptive approach /
- Parameter selection.

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图 1 实孔径雷达波束扫描探测工作模式与角度维回波数据示意图

Figure 1. Beam scanning working mode and echo data in the azimuthal direction of RAR

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图 2 传统IAA算法与IAA-Net深度网络结构对比

Figure 2. Comparison of the traditional IAA algorithm and the proposed IAA-Net method

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图 3 IAA-Net深度网络迭代结构

Figure 3. Iterative framework of the proposed IAA-Net method

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图 4 目标重建更新模块

Figure 4. Target reconstructing and updating module

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图 5 参数训练更新模块

Figure 5. Parameter training and updating module

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图 6 部分训练样本示例

Figure 6. Examples of some training samples

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图 7 V形目标不同超分辨成像方法处理结果对比

Figure 7. Comparison of different super-resolution imaging methods for V points

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图 8 V形目标不同超分辨成像方法处理结果剖面对比

Figure 8. Result profile comparison of different super-resolution imaging methods for V points

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图 9 点阵目标不同超分辨成像方法结果对比

Figure 9. Comparison of different super-resolution imaging methods for multiple point targets

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图 10 点阵目标不同超分辨成像方法结果剖面对比

Figure 10. Profile comparison of different super-resolution imaging methods for multiple point targets

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图 11 不同超分辨方法在不同信噪比下重建结果的平均主瓣宽度

Figure 11. Average mainlobe width of different super-resolution methods under different SNRs

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图 12 不同方位向采样点数下超分辨处理运行时间

Figure 12. Operational time of different super-resolution methods with different azimuthal samples

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图 13 不同算法实测数据角超分辨结果

Figure 13. Experimental results of different superresolution methods

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图 14 环扫监视雷达不同算法实测数据角超分辨结果

Figure 14. Experimental results of different superresolution methods for circular scanning radar

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表 1 雷达系统仿真参数

Table 1. Radar system simulation parameters

参数	数值
扫描速度	50°/s
扫描范围	–8°～+8°
脉冲重复频率	1000 Hz
主瓣波束宽度	5.1°
载波频率	9.6 GHz
信号带宽	45 MHz
信号时宽	2 μs
采样率	90 MHz
平台速度	30 m/s

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表 2 不同超分辨方法的MSE对比

Table 2. MSE comparison of different super-resolution methods

场景\MSE	Tikhonov L2方法	分裂Bregman L1方法	IAA方法($\gamma = $0.2)	IAA方法($\gamma = $0.05)	IAA-Net方法
V型场景	26.4810	3.3306	13.2678	8.0262	2.2932

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