针对传统的基于傅里叶变换和匹配滤波实现的chirp scaling(CS)成像算法中多普勒参数随斜距变化以及成像分辨率低的问题，提出利用分数阶傅里叶变换(FRFT)对CS成像算法进行优化。首先建立斜视合成孔径雷达(SAR)回波信号模型，理论推导利用FRFT代替匹配滤波进行信号压缩。针对方位向最优旋转角的搜索问题，对得到的图像基于加权最小熵建立代价函数，利用动量法的梯度下降优化算法进行迭代计算，最终得到分辨率更高的SAR图像。为验证算法的有效性，分别在点目标仿真数据和实测SAR数据集上进行实验。结果表明，与传统CS成像算法相比，该算法的成像结果成像主瓣宽度更窄、旁瓣更低、成像更加清晰。
In order to solve the problem of Doppler parameters varying with skew and low image resolution in the traditional chirp scaling (CS) imaging algorithm based on Fourier transform and matched filtering, an algorithm to optimize CS imaging algorithm using fractional Fourier transform (FRFT) is proposed. Firstly, the echo signal model of squint synthetic aperture radar (SAR) is established, and the echo signal model is derived using FRFT instead of matched filtering. To search for the optimal azimuth rotation angle, the cost function of the image is established according to the weighted minimum entropy, and the gradient descent optimization algorithm of the momentum method is used for iterative calculation. Finally, a higher-resolution SAR image is obtained. To verify the effectiveness of the algorithm, experiments were carried out on point target simulation data and measured SAR data sets respectively. The results show that, compared with the traditional CS imaging algorithm, the proposed algorithm achieves a narrower main lobe width, lower sidelobe, and clearer images.
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