Dept. of Electr. Eng., Ohio State Univ., Columbus, OH.
IEEE Trans Image Process. 1997;6(1):79-91. doi: 10.1109/83.552098.
High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.
高频雷达对人造目标的测量主要由孤立散射中心的回波主导，例如角和平板。对这些散射中心特征的描述为自动目标识别（ATR）提供了一种简洁、物理相关的信号表示。在本文中，我们提出了一种基于雷达回波参数模型的特征提取框架。该模型的动机是由几何绕射理论预测的散射行为。对于每个散射中心，对模型参数进行稳健的统计估计，可以提供包括位置、几何形状和极化响应在内的高分辨率属性。我们对散射模型进行了统计分析，以描述特征不确定性，并提供了用于特征估计的最小二乘算法。我们调查了简化模型的现有算法，并推导出采用简化模型所产生的误差的界。还给出了一种模型阶数选择算法，并给出了用于球形不变随机杂波中极化响应分类的 M 元广义似然比检验。
