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针对大气扰动及飞行平台不稳引起机载合成孔径雷达(SAR)图像散焦的问题,提出了一种新的自聚焦算法来估算运动误差。该算法是基于运动误差函数及场景散射函数平滑特性的差异来进行估计的,利用复对数变换将图像的幅度与相位信息分离,进而分别对相位及幅度信息进行处理。运动误差通常为慢变函数,而场景的散射信息具有某种随机特性。因此,经过复对数变换后,运动误差及散射信息可以通过滤波器进行分离,将相位中的随机噪声去除,从而保留了慢变的运动误差函数。为了去除噪声信息,需要建立一个平滑滤波器,利用Daubechies小波的尺度函数构造Riesz基向量,从而建立了正交子空间,通过所建立信号子空间及噪声子空间组建平滑滤波器,最终可以获得准确的运动误差。在实验部分,分别利用仿真数据及实测数据对本文方法进行验证,最终结果分析表明该方法具有很高的估计精度及执行效率。
Aiming at the problem of airborne SAR and defocused airborne Synthetic Aperture Radar (SAR) images, a new self-focusing algorithm is proposed to estimate the motion error. The algorithm is based on the difference between the motion error function and the smoothing characteristics of the scene scattering function. The complex logarithm transform is used to separate the amplitude and phase information of the image, and then the phase and amplitude information are processed separately. The motion error is usually a slow variable function, and the scattering information of the scene has some random characteristics. Therefore, after the complex logarithm transform, the motion error and scattering information can be separated by the filter, and the random noise in the phase is removed, so the slowly changing motion error function is retained. In order to remove the noise information, a smoothing filter needs to be set up to construct the Riesz basis vector by using the scale function of Daubechies wavelet to establish the orthogonal subspace, and the smoothing filter can be constructed through the established signal subspace and the noise subspace to obtain accurate Motion error. In the experimental part, the simulation results and the measured data are respectively used to verify the proposed method. The final result shows that the proposed method has high estimation accuracy and efficiency.