关于高分辨率距离剖面的变异性

S. Doo, G. E. Smith, C. Baker
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引用次数: 2

摘要

雷达目标分类通常使用高距离分辨率轮廓的特征集作为分类器的输入。然而,rage profile通常表现出可变性,随后传递给分类中使用的特征向量。由于特征向量的方差太大,而特征向量之间的平均距离太小,导致分类性能下降。在本文中,我们研究了这种变化的根本原因。具体来说,我们通过建模、仿真和实验证明:(i)匹配滤波器的输出是沿目标长度分布的散射体相对位置的敏感函数,(ii)散射体相位中心的表观位置是视角的敏感函数,即使在一小部分程度的方向变化上也会引起显著的变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On variability in high resolution range profiles
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.
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