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    • 基于变分模态分解与优选的超高分辨ISAR成像微多普勒抑制方法

    LI Zhongyu, GUI Liang,HAI Yu,WU Junjie,WANG Dangwei,WANG Anle,YANG Jianyu

    Journal of Radars（2024）

    Cited 0|Views3
    Abstract
    逆合成孔径雷达(ISAR)在对空中目标成像时，目标自身的转动、振动等局部微动将产生微多普勒效应，回波将附加额外的多普勒调制，造成频谱展宽。在超高分辨条件下，这一微动特性将会影响主体散射点的聚焦，导致目标图像局部散焦模糊，严重影响成像质量。并且，微多普勒相位还具有时变非平稳特性，难以从ISAR目标回波中准确估计或分离出微多普勒。为了解决上述问题，该文利用目标主体回波和微多普勒分量的时频分布差异，提出一种基于变分模态分解(VMD)与优选的非参数化方法抑制了回波中的微多普勒分量，消除了微多普勒对成像的影响，获得超高分辨率的无人机ISAR成像结果。该文首先引入VMD算法并将其扩展到复数域，将ISAR目标回波数据沿方位向分解为若干个中心频率均匀分布于多普勒采样带宽中的模函数，在此基础上利用图像熵指标优化分解参数和筛选成像模态，以保证微多普勒的良好抑制和主体回波的较完整保留。与现有基于经验模态分解(EMD)和局部均值分解(LMD)的方法相比，所提方法在超大带宽条件下对旋翼微动引起的微多普勒干扰有着更为出色的抑制效果，而且对机身部分的保留更为完整。最后，通过仿真对比和超宽带微波光子ISAR无人机实测数据处理，证明了该文所提方法的有效性和优势。
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    Key words
    Inverse Synthetic Aperture Rada(ISAR),Micro-Doppler,Variable Mode Decomposition(VMD),Entropy minimization,Time-frequency analysis
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    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
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    【要点】：本文提出了一种基于变分模式分解（VMD）和模式优化的超高分辨率ISAR微多普勒抑制方法，通过分离目标回波与微多普勒分量，提高了成像质量。
    

    【方法】：采用VMD算法将ISAR回波在方位向分解为多个模式函数，并通过图像熵指数优化分解参数，选择成像模式以抑制微多普勒信号。
    

    【实验】：通过仿真和无人机测量的超宽带微波光子数据处理验证了方法的有效性，实验数据集来源于无人机的测量。