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On variability in high resolution range profiles

机译:高分辨率范围型材的变化

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Radar target classification often uses feature sets derived from high range resolution profiles as an input to a classifier. However, rage profiles typically exhibit variability that is subsequently passed on to the feature vectors used in classification. This results in degraded classification performance as the variance in the feature vectors is too large and the mean distance between feature vector too small. In this paper we examine the fundamental causes of such variability. Specifically we demonstrate via modeling, simulation and experimentation that (i) the output of the matched filter is a sensitive function of the relative position of scatters distributed along the length of a target and (ii) the apparent location of a scatterers phase center is a sensitive function of viewing angle causing significant variability even over orientation changes of a fraction of a degree.
机译:雷达目标分类通常使用从高范围分辨率配置文件导出的特征集作为对分类器的输入。然而,愤怒轮廓通常表现出随后传递到分类中使用的特征向量的可变性。这导致降级的分类性能随着特征向量的方差太大并且特征向量之间的平均距离太小。在本文中,我们研究了这种变异性的根本原因。具体地,我们通过建模,仿真和实验来证明(i)匹配滤波器的输出是沿着目标的长度分布的散射的相对位置的敏感函数,并且(ii)散射体相位中心的表观位置是a观察角度的敏感函数导致显着的变化即使在一分度的方向变化也会变化。

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