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

毛德庆, 杨建宇, 杨明杰, 等. IAA-Net:一种实孔径扫描雷达迭代自适应角超分辨成像方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24063
引用本文: 毛德庆, 杨建宇, 杨明杰, 等. IAA-Net:一种实孔径扫描雷达迭代自适应角超分辨成像方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24063
MAO Deqing, YANG Jianyu, YANG Mingjie, et al. IAA-Net: An iterative adaptive approach for angular super-resolution imaging of real aperture scanning radar[J]. Journal of Radars, in press. doi: 10.12000/JR24063
Citation: MAO Deqing, YANG Jianyu, YANG Mingjie, et al. IAA-Net: An iterative adaptive approach for angular super-resolution imaging of real aperture scanning radar[J]. Journal of Radars, in press. 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
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,博士,教授,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,博士,副研究员,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,博士,研究员,主要研究方向为信号处理和雷达成像等

    黄钰林,博士,教授,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

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

Funds: The National Natural Science Foundation of China (62301131), The China Postdoctoral Science Foundation (BX20220055, 2022M720667)
More Information
  • 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,该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。

     

  • 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

    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

    图  3  IAA-Net深度网络迭代结构

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

    图  4  目标重建更新模块

    Figure  4.  Target reconstructing and updating module

    图  5  参数训练更新模块

    Figure  5.  Parameter training and updating module

    图  6  部分训练样本示例

    Figure  6.  Examples of some training samples

    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

    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

    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

    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

    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

    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

    图  13  不同算法实测数据角超分辨结果

    Figure  13.  Experimental results of different super-resolution methods

    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

    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
    下载: 导出CSV

    表  2  V型场景不同超分辨方法的MSE对比

    Table  2.   MSE comparison of different super-resolution methods

    方法 MSE
    Tikhonov L2方法 26.4810
    分裂Bregman L1方法 3.3306
    IAA方法($\gamma = $0.2) 13.2678
    IAA方法($\gamma = $0.05) 8.0262
    所提IAA-Net方法 2.2932
    下载: 导出CSV
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  • 收稿日期:  2024-04-09
  • 修回日期:  2024-06-23
  • 网络出版日期:  2024-07-22

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