Deep neural networks have shown promise in the radar-based human activity analysis application. Different from existing deep learning models that take either micro-Doppler spectrograms or range profiles as their input, the proposed method can process micromotion signatures in a 3-D way. In this letter, we first transform radar echoes into range-Doppler (RD) time points and then directly process the point sets via a designed 3-D network called the RD PointNet. In fact, our point model is a discrete representation of the motion trajectory. Through this quantitative model, we can use the 3-D network to simultaneously capture human motion profiles and temporal variations. The motion capture simulations and ultrawideband radar measurements show that the proposed framework can achieve superior classification accuracy and noise robustness when compared with image-based methods.

中文翻译：

---

使用距离多普勒时间点进行人类行为分析的三维深度学习框架

深度神经网络在基于雷达的人类活动分析应用中显示出前景。与现有的将微多普勒频谱图或距离剖面作为输入的深度学习模型不同，所提出的方法可以以 3-D 方式处理微动特征。在这封信中，我们首先将雷达回波转换为距离多普勒 (RD) 时间点，然后通过称为 RD PointNet 的设计 3-D 网络直接处理点集。事实上，我们的点模型是运动轨迹的离散表示。通过这个定量模型，我们可以使用 3-D 网络同时捕捉人体运动轮廓和时间变化。