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针对现有的Spatially Variant Apodization(SVA)算法不能有效抑制旁瓣或损失主瓣能量的问题,该文提出了一种改进的SVA算法.该算法把传统的滤波器从3点扩展到5点,并且根据采样率的不同,设定相应的滤波器参数,得到满足约束优化理论的最优解.改进的SVA算法能够与合成孔径雷达(Synthetic Aperture Radar,SAR)成像算法相结合,在距离压缩和方位压缩中,分别利用改进的SVA算法来抑制旁瓣.该算法适用于任意奈奎斯特采样率,既能有效地抑制旁瓣,又能保持主瓣的能量和信号的高分辨率.实验结果表明,与传统的频域加窗方法相比,该方法能够在保持图像高分辨率的前提下,更有效地抑制旁瓣.
Aiming at the problem that the existing Spatially Variant Apodization (SVA) algorithm can not effectively suppress the side lobe or lose the main lobe energy, an improved SVA algorithm is proposed in this paper, which expands the traditional filter from 3 to 5, According to the different sampling rate, the corresponding filter parameters are set to obtain the optimal solution which satisfies the constraint optimization theory.The improved SVA algorithm can be combined with the synthetic aperture radar (SAR) imaging algorithm, In the aspect of orientation compression, the sidelobe is suppressed by the improved SVA algorithm.The algorithm is suitable for any Nyquist sampling rate, which not only can effectively suppress the sidelobe, but also can maintain the main lobe energy and the high resolution signal.Experiment The results show that compared with the traditional windowing method in frequency domain, this method can suppress the sidelobe more effectively while maintaining the high resolution of the image.