Aperture‐Synthesis Radar Imaging With Compressive Sensing for Ionospheric Research
Aperture‐Synthesis Radar Imaging With Compressive Sensing for Ionospheric Research
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DOI：
10.1029/2019rs006805
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发表时间：
2019-06
期刊：
影响因子：
1.6
通讯作者：
D. Hysell;Pragya Sharma;M. Urco;M. Milla
中科院分区：
文献类型：
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作者：
D. Hysell;Pragya Sharma;M. Urco;M. Milla
涉及压缩传感的逆方法在电离层中的场对准等离子体密度不规则性的雷达后向散射的二维孔径合成成像的应用中进行了测试。我们考虑基追踪去噪，实现快速迭代收缩阈值算法，和正交匹配追踪（OMP）与小波基的评价。这些方法与两种更传统的优化方法进行了比较，这种方法植根于熵最大化（MaxENT）和自适应波束形成（线性约束最小方差或通常称为“Capon方法”）。被认为是一个扩展的电离层雷达目标相对应的合成数据。我们发现，MaxENT优于其他方法的能力，以恢复图像的扩展目标具有广泛的动态范围。快速迭代收缩阈值算法表现相当不错，但不能再现目标的全动态范围。它也是测试的方法中计算量最大的。OMP在计算上非常快，但在此应用中容易出现高度混乱。我们还指出，这里使用的MaxENT公式在某些方面与OMP非常相似，区别在于前者从观测矩阵中提取的基向量重建图像的对数，而不是图像本身。在这方面，MaxENT可以被认为是一种压缩感测形式。
Inverse methods involving compressive sensing are tested in the application of two‐dimensional aperture‐synthesis imaging of radar backscatter from field‐aligned plasma density irregularities in the ionosphere. We consider basis pursuit denoising, implemented with the fast iterative shrinkage thresholding algorithm, and orthogonal matching pursuit (OMP) with a wavelet basis in the evaluation. These methods are compared with two more conventional optimization methods rooted in entropy maximization (MaxENT) and adaptive beamforming (linearly constrained minimum variance or often “Capon's Method.”) Synthetic data corresponding to an extended ionospheric radar target are considered. We find that MaxENT outperforms the other methods in terms of its ability to recover imagery of an extended target with broad dynamic range. Fast iterative shrinkage thresholding algorithm performs reasonably well but does not reproduce the full dynamic range of the target. It is also the most computationally expensive of the methods tested. OMP is very fast computationally but prone to a high degree of clutter in this application. We also point out that the formulation of MaxENT used here is very similar to OMP in some respects, the difference being that the former reconstructs the logarithm of the image rather than the image itself from basis vectors extracted from the observation matrix. MaxENT could in that regard be considered a form of compressive sensing.