We propose a two‐dimensional (2D) scattering‐center‐extraction (SCE) method using sparse recovery based on the compressive‐sensing theory, even with data missing from the received radar cross‐section (RCS) dataset. First, using the proposed method, we generate a 2D grid via adaptive discretization that has a considerably smaller size than a fully sampled fine grid. Subsequently, the coarse estimation of 2D scattering centers is performed using both the method of iteratively reweighted least square and a general peak‐finding algorithm. Finally, the fine estimation of 2D scattering centers is performed using the orthogonal matching pursuit (OMP) procedure from an adaptively sampled Fourier dictionary. The measured RCS data, as well as simulation data using the point‐scatterer model, are used to evaluate the 2D SCE accuracy of the proposed method. The results indicate that the proposed method can achieve higher SCE accuracy for an incomplete RCS dataset with missing data than that achieved by the conventional OMP, basis pursuit, smoothed L0, and existing discrete spectral estimation techniques.

中文翻译：

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基于压缩感知的二维散射中心提取不完整的RCS数据

我们提出了一种基于压缩感知理论的使用稀疏恢复的二维（2D）散射中心提取（SCE）方法，即使接收到的雷达横截面（RCS）数据集中缺少数据也是如此。首先，使用提出的方法，我们通过自适应离散化生成2D网格，该网格的大小比完全采样的精细网格小得多。随后，使用迭代加权最小二乘法和通用峰值发现算法对二维散射中心进行粗略估计。最后，使用来自自适应采样傅里叶字典的正交匹配追踪（OMP）过程对2D散射中心进行精细估计。测得的RCS数据以及使用点散射器模型的模拟数据均用于评估所提出方法的二维SCE精度。