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

Ultrahigh-resolution ISAR Micro-Doppler Suppression Methodology Based on Variational Mode Decomposition and Mode Optimization

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。

The imaging of aerial targets using Inverse Synthetic Aperture Radar(ISAR)is affected by micro-Doppler effects resulting from localized micromotions,such as rotation and vibration.These effects introduce additional Doppler frequency modulation into the echo,leading to spectral broadening.Under ultrahigh-resolution conditions,these micromotions interfere with the focusing process of subject scatterers,resulting in images with poor focus showing significantly reduced quality.Furthermore,micro-Doppler signals exhibit temporal variability and nonstationary characteristics,posing difficulties in their estimation and differentiation from the echo.To address these challenges,this paper proposes a nonparametric method based on Variational Mode Decomposition(VMD)and mode optimization to separate the echo of the subject from micro-Doppler components.This separation is achieved by utilizing differences in their respective time-frequency distributions.This methodology mitigates the effect of micro-Doppler signals on the echo and obtains imaging results of a drone with ultrahigh-resolution.The VMD algorithm is introduced and subsequently extended to the complex domain.The method entails the decomposition of the ISAR echo along the azimuth direction into several mode functions distributed uniformly across the Doppler sampling bandwidth.Subsequently,image entropy indices are employed to optimize the decomposition parameters and select the imaging modes.This ensures the effective suppression of micro-Doppler signals and preservation of the subject echo.Compared to existing methods based on Empirical Mode Decomposition(EMD)and Local Mean Decomposition(LMD),the proposed method exhibits superior performance in suppressing image blurring caused by micro-Doppler effects while ensuring complete retention of fuselage details.Furthermore,the effectiveness and advantages of the proposed method are validated through simulations and processing of ultrawideband microwave photonic data obtained from drone measurements.