摘要
压缩传感SAR成像能够大量减小采样率和数据量,但只对稀疏场景有效。该文提出基于小波包训练稀疏表示基的压缩传感SAR成像方法。该方法通过对同类型的SAR图像进行小波包训练,在小波包库中选择能够稀疏表示该类SAR场景的稀疏表示基,并通过求解1l范数最小化问题重构SAR场景反射系数。文中提出的方法在严重降采样下仍能够实现无模糊的SAR成像,仿真数据成像结果表明该文方法具有较好的效果。
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 11 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.
出处
《雷达学报（中英文）》
CSCD
2013年第2期218-225,共8页
Journal of Radars
基金
国家自然科学基金(11171014)
国家973计划项目(2010CB731900)资助课题
关键词
压缩传感
SAR成像
小波包
稀疏表示
稀疏度
Compressive sensing
SAR imaging
Wavelet packet
Sparse representation
Sparsity
