基于压缩感知的矩阵型联合SAR成像与自聚焦算法
发布时间：2018-10-23 12:49
【摘要】：模型准确情况下,压缩感知在合成孔径雷达成像中得到良好应用;但在实际情况中,模型会存在一定误差,这些误差造成图像偏离真实位置、引起散焦降低成像质量.本文提出一种矩阵型联合CS-SAR成像与自聚焦算法,该算法在CS-SAR成像重构方法方面,基于光滑l_0范数方法提出了矩阵型正则化光滑l_0范数重构方法,该方法具有较强容错能力并能直接重构矩阵型信号,能克服现有联合CS-SAR成像与自聚焦算法在计算效率方面的缺陷.最后,通过仿真验证了所提算法的有效性.
[Abstract]:Compression sensing is well applied in synthetic Aperture Radar (SAR) imaging when the model is accurate, but in practice, there will be some errors in the model, which cause the image to deviate from the real position and defocus to reduce the imaging quality. In this paper, a matrix type combined CS-SAR imaging and autofocus algorithm is proposed. In the aspect of CS-SAR reconstruction, a matrix regularized smooth l0-norm reconstruction method is proposed based on the smooth ls-stack norm method. This method has strong fault-tolerant ability and can directly reconstruct matrix signals. It can overcome the shortcomings of the existing joint CS-SAR imaging and self-focusing algorithms in computing efficiency. Finally, the effectiveness of the proposed algorithm is verified by simulation.
【作者单位】： 河北师范大学物理科学与信息工程学院;北京理工大学信息与电子学院;
【基金】：河北省高等学校自然科学重点项目(No.ZD2016031) 河北师范大学自然科学科研基金(No.L2016B06,No.L2010Y01)
【分类号】：TN957.52
本文编号：2289295
[Abstract]:Compression sensing is well applied in synthetic Aperture Radar (SAR) imaging when the model is accurate, but in practice, there will be some errors in the model, which cause the image to deviate from the real position and defocus to reduce the imaging quality. In this paper, a matrix type combined CS-SAR imaging and autofocus algorithm is proposed. In the aspect of CS-SAR reconstruction, a matrix regularized smooth l0-norm reconstruction method is proposed based on the smooth ls-stack norm method. This method has strong fault-tolerant ability and can directly reconstruct matrix signals. It can overcome the shortcomings of the existing joint CS-SAR imaging and self-focusing algorithms in computing efficiency. Finally, the effectiveness of the proposed algorithm is verified by simulation.
【作者单位】： 河北师范大学物理科学与信息工程学院;北京理工大学信息与电子学院;
【基金】：河北省高等学校自然科学重点项目(No.ZD2016031) 河北师范大学自然科学科研基金(No.L2016B06,No.L2010Y01)
【分类号】：TN957.52
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