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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Superresolution Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on Two-Dimensional Compressive Sensing
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Superresolution Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on Two-Dimensional Compressive Sensing

机译：基于二维压缩感测的超分辨率向下看线阵三维SAR成像

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摘要

For downward-looking linear array 3-D synthetic aperture radar (SAR), the azimuth and cross-track resolution are unacceptable due to the length limitation of synthetic aperture and linear array. Hence, superresolution reconstruction algorithms are desired. Since the signal to be reconstructed is sparse on the 2-D azimuth–cross-track plane, it is quite suitable to apply the compressive sensing theory to obtain the images. The existed imaging algorithms for downward-looking linear array 3-D SAR are based on 1-D compressive sensing, which could bring the couple effect between different directions. To solve this problem, a novel 3-D imaging algorithm based on 2-D compressive sensing is proposed in this paper. Instead of converting the sparse reconstruction of 2-D matrix signals to the sparse reconstruction of 1-D vectors, the proposed algorithm directly reconstructs the 2-D sparse signals on overcomplete dictionaries with separable atoms. It not only provides superresolution performance, but also reduces the storage of data acquisition and processing. Furthermore, a definition of joint sparse sampling strategy is given to reconstruct the measurement matrices for further improving the computational efficiency of the imaging algorithm. Moreover, in order to investigate the limits of the proposed algorithm, the theory analysis of Cram $acute{e}$r–Rao bound is derived and compared with the standard deviation. Finally, numerical simulations under the noise scenarios and the principle prototype experiment on real data are shown to demonstrate the validity and the limits of the proposed algorithm.
机译：对于向下看的线性阵列3-D合成孔径雷达（SAR），由于合成孔径和线性阵列的长度限制，方位角和跨轨分辨率是不可接受的。因此，需要超分辨率重建算法。由于要重建的信号在二维方位角交叉轨迹平面上稀疏，因此非常适合应用压缩感测理论来获取图像。现有的向下看的线性阵列3-D SAR成像算法基于一维压缩感测，可以在不同方向之间产生耦合效应。针对这一问题，提出了一种基于二维压缩感知的新型3-D成像算法。所提出的算法不是将二维矩阵信号的稀疏重建转换为一维矢量的稀疏重建，而是直接在具有可分离原子的超完备字典上直接重建二维稀疏信号。它不仅提供超分辨率性能，而且减少了数据采集和处理的存储量。此外，给出了联合稀疏采样策略的定义，以重构测量矩阵，以进一步提高成像算法的计算效率。此外，为了研究该算法的局限性，推导了Cram $ acute {e} $ r-Rao界的理论分析，并将其与标准偏差进行比较。最后，在噪声场景下进行了数值模拟，并对真实数据进行了原理原型实验，证明了该算法的有效性和局限性。

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