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A Three-Dimensional Deep Learning Framework for Human Behavior Analysis Using Range-Doppler Time Points
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DOI：10.1109/LGRS.2019.2930636.png)
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摘要
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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.
Keyword:
Solid modeling
Radar
Doppler effect
Deep learning
Computational modeling
Analytical models
Trajectory
Geometric deep learning
graph network
human activity recognition
micro-Doppler effect
point cloud
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Information-Theoretic Feature Selection for Human Micro-Doppler Signature Classification基于信息论特征选择的人体微多普勒特征分类
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