基于小波包算法的压缩感知SAR成像方法

Q2 Physics and Astronomy
Shi Yan, Di-rong Chen
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引用次数: 0

摘要

压缩感知SAR成像可以显著降低采样率和所需的数据量,但只有在SAR场景反射系数稀疏的情况下才有必要。提出了一种基于小波包稀疏表示的压缩感知SAR成像方法。小波包算法通过训练相同类型的SAR图像,选择最稀疏的SAR场景表示。通过求解最小1 l范数优化,可以重建SAR场景反射系数。即使使用较少的样本,该方法也可以产生清晰的SAR图像。SAR数据仿真实验验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Compressive Sensing SAR Imaging Approach Based on Wavelet Package Algorithm
Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data required, but it is essential only in the case where the reflection coefficients of a SAR scene are sparse. This paper proposes a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR images can be produced with the proposed method, even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.
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来源期刊
雷达学报
雷达学报 Physics and Astronomy-Instrumentation
CiteScore
4.10
自引率
0.00%
发文量
882
期刊介绍: Information not localized
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