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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >SAR Imaging With Undersampled Data via Matrix Completion
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SAR Imaging With Undersampled Data via Matrix Completion

机译：通过矩阵补全实现欠采样数据的SAR成像

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摘要

High-resolution synthetic aperture radar (SAR) imagery of a wide area of surveillance is a difficult large-data problem. In the past few years, researchers have applied compressive sensing (CS) to SAR, as it exploits redundancy in signals. To further extend the sparse problem from the vector to the matrix, a new theory called matrix completion (MC) has attracted much attention, which can complete a matrix from a small set of corrupted entries based on the assumption that the matrix is essentially of low rank. Inspired by this technique, a novel SAR imaging algorithm is proposed in this letter to deal with the undersampled data. After representing the data of a range cell as a matrix, the phase is compensated to keep the matrix holding the property of low rank. Subsequently, MC can be utilized to recover the full-aperture data in the new constructed matrix. Since the data are completely unsampled in the corresponding azimuth cells, the proposed method has effectively conquered the restriction of previous applications that each received channel must have a small number of samples. The final results in both simulation and real-data experiments show that the targets can be well focused even in the scenario of discarding a large percentage of the received pulses. Moreover, when compared with CS, the method is not required to design the complicated measurement matrix.
机译：大范围监视的高分辨率合成孔径雷达（SAR）图像是一个困难的大数据问题。在过去的几年中，研究人员将压缩感知（CS）应用于SAR，因为它利用了信号的冗余性。为了进一步将稀疏问题从向量扩展到矩阵，一种称为矩阵完成（MC）的新理论引起了人们的广泛关注，该理论可以在假设矩阵本质上为低的前提下，从一小组损坏的条目中完成矩阵秩。受此技术启发，本文提出了一种新颖的SAR成像算法来处理欠采样数据。在将范围单元的数据表示为矩阵之后，对相位进行补偿，以保持矩阵保持低秩的特性。随后，可以利用MC恢复新构造矩阵中的全孔径数据。由于数据在相应的方位角像元中完全未采样，因此该方法有效地克服了先前应用的局限性，即每个接收的通道必须具有少量采样。在模拟和实际数据实验中的最终结果表明，即使在丢弃大部分接收脉冲的情况下，目标也可以很好地聚焦。而且，与CS相比，不需要设计复杂的测量矩阵的方法。

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