论文标题
使用Ultrawideband雷达雷达的个人识别，步行和坐着运动和卷积神经网络
Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network
论文作者
论文摘要
这项研究提出了一种个人识别技术，该技术将用两层卷积神经网络应用机器学习应用于从运动中的目标人的雷达回波获得的频谱图像。使用Ultrawideband雷达系统测量了六个参与者的步行和坐着运动。将时频分析应用于雷达信号，以生成包含与肢体运动相关的微型多普勒组件的频谱图。使用带有个人标签的频谱图培训了卷积神经网络，以实现基于雷达的个人识别。对个人识别精度进行了实验评估，以证明所提出的技术的有效性。
This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique.
