In this paper, we propose a Doppler-spectrum feature-based human-vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.

中文翻译：

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基于多普勒频谱特征的FMCW雷达传感器基于机器学习的人机分类方案。

在本文中，我们为FMCW（调频连续波）雷达传感器提出了一种基于多普勒频谱特征的人车分类方案。基于两个连续帧中多普勒频谱的特性，我们介绍了三个新颖的特征，分别称为散射点计数，散射点差和幅度差率特征。我们还使用SVM（支持向量机）和BDT（二进制决策树）来训练和验证上述三个功能。我们使用24 GHz FMCW雷达前端模块和实时数据采集模块测量信号，并从现场行走的人和行驶中的车辆中提取了三个特征。然后，我们使用SVM和BDT反复测量该算法的分类决策率，