Sparsity-Based Autofocus for Undersampled Synthetic Aperture Radar
Sparsity-Based Autofocus for Undersampled Synthetic Aperture Radar
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DOI：
10.1109/taes.2014.120502
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发表时间：
2014-04-01
影响因子：
4.4
通讯作者：
Davies, Mike
中科院分区：
文献类型：
--
作者：
Kelly, Shaun;Yaghoobi, Mehrdad;Davies, Mike
受压缩感知和稀疏恢复领域的启发，已经提出了用于相位历史欠采样时的合成孔径雷达图像重建的非线性算法。这些算法假设系统采集模型的精确知识。在本文中，我们调查的影响，采集模型相位误差时，相位历史欠采样。我们表明，标准的自动对焦方法，这是作为一个后处理步骤的重建图像，通常是不合适的。而不是在后处理中应用自动对焦，我们提出了一种算法，纠正图像重建过程中的相位误差。该算法的性能进行了定量和定性研究，通过数值模拟的两个实际情况下，相位历史包含相位误差和欠采样。
Motivated by the field of compressed sensing and sparse recovery, nonlinear algorithms have been proposed for the reconstruction of synthetic-aperture-radar images when the phase history is undersampled. These algorithms assume exact knowledge of the system acquisition model. In this paper we investigate the effects of acquisition-model phase errors when the phase history is undersampled. We show that the standard methods of autofocus, which are used as a postprocessing step on the reconstructed image, are typically not suitable. Instead of applying autofocus in postprocessing, we propose an algorithm that corrects phase errors during the image reconstruction. The performance of the algorithm is investigated quantitatively and qualitatively through numerical simulations on two practical scenarios where the phase histories contain phase errors and are undersampled.