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Ultrahigh-resolution ISAR Micro-Doppler Suppression Methodology Based on Variational Mode Decomposition and Mode Optimization
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摘要: 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。Abstract: 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.
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图 1 几种典型目标所占距离门数目随信号带宽的变化
Figure 1. Variation of the number of range cells for several typical targets with bandwidth
图 3 含微动部件目标的ISAR几何模型
Figure 3. ISAR geometric model of targets with micro-motion components
图 4 微多普勒信号的STFT谱和频谱图
Figure 4. STFT spectrogram and spectrum of the micro-Doppler signal
图 6 所提微多普勒抑制方法流程图
Figure 6. Flow diagram of the proposed micro-Doppler suppression method
图 8 各种算法处理后的成像结果对比
Figure 8. Comparison of the imaging results processed by various algorithms
图 9 各种算法处理后的STFT谱对比
Figure 9. Comparison of the STFT spectrogram processed by various algorithms
图 10 不同信噪比下的仿真成像结果对比
Figure 10. Comparison of simulated imaging results at different SNR
图 12 各种算法处理无人机数据的成像结果对比
Figure 12. Comparison of the effects of different algorithms on the processing of measured data
图 13 处理后旋翼中心点对应方位STFT谱
Figure 13. STFT spectrogram of the center point on the rotor after processing
图 14 旋翼中心点残余相位与方位剖面对比图
Figure 14. Azimuth residual phase and profile comparison of the center point on the rotor
表 1 仿真参数表
Table 1. Simulation parameter table
参数 数值 载波频率${f_{\mathrm{c}}}$ 35 GHz 信号带宽${B_{\mathrm{r}}}$ 10 GHz 脉冲重复频率${\text{PRF}}$ 833 Hz 成像积累时间 2.46 s 无人机运动速度 5 m/s 目标俯仰角 15° 旋翼个数 4 单旋翼叶片个数 2 叶片长度l 0.1 m 单叶片微动散射点个数 5 旋翼转速${\omega _{\mathrm{r}}}$ 20$\pi $ rad/s 
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表 2 各种算法处理后的图像质量比较
Table 2. Comparison of image quality processed by various algorithms
算法 能量相似比(区域1) 能量相似比(区域2) 图像熵 对比度 锐度 直接成像 8.64 3.87 8.84 18.65 1.03E+10 PGA 9.60 4.11 8.75 20.19 9.54E+09 基于EMD 0.22 1.23 8.31 21.44 8.72E+09 基于LMD 0.16 0.84 8.28 20.45 8.58E+09 本文方法 0.15 0.16 8.16 22.71 9.15E+09
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表 3 六旋翼无人机实测实验参数表
Table 3. Experimental parameter table for the hexacopter UAV
参数 数值 载波频率${f_{\mathrm{c}}}$ 35 GHz 信号带宽${B_{\mathrm{r}}}$ 10 GHz 脉冲重复频率${\text{PRF}}$ 833 Hz 成像积累时间 0.72 s 目标俯仰角 15°
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表 4 六旋翼无人机成像质量比较
Table 4. Comparison of imaging quality of the hexacopter
算法 图像熵 对比度 锐度 直接成像 8.19 22.12 5.44E+11 PGA 7.98 23.59 2.90E+11 基于EMD 7.63 23.28 4.17E+11 基于LMD 7.59 23.14 4.11E+11 基于CVMD 7.67 23.66 4.05E+11 本文方法 7.05 25.79 4.33E+11
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