A comparison of 1D and 2D algorithms for radar target classification

L. Novak
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引用次数: 31

Abstract

The use of high-resolution radar measurement data (35 GHz) from four ground vehicles (bulldozer, Dodge Power Wagon, Dodge Van, and Camaro) to evaluate the performance of several 1D and 2D classifiers is discussed. The 1D classifiers use high-resolution range profiles to classify targets; the 2D classifier uses high-resolution inverse synthetic aperture radar (ISAR) images to classify targets. Classification performance results using the 1D and 2D algorithms are presented, and it is shown that the 2D algorithm performed best.<>
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雷达目标分类的一维和二维算法比较
讨论了使用四种地面车辆(推土机、道奇动力旅行车、道奇货车和科迈罗)的高分辨率雷达测量数据(35 GHz)来评估几种1D和2D分类器的性能。一维分类器使用高分辨率距离像对目标进行分类;二维分类器采用高分辨率逆合成孔径雷达(ISAR)图像对目标进行分类。给出了使用一维和二维算法的分类性能结果,并表明二维算法的分类性能最好。
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