基于步行与坐姿动作的超宽带雷达测量及卷积神经网络的个人身份识别
Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network
AI总结：
本研究提出一种基于超宽带雷达与双层卷积神经网络的个人身份识别技术，通过对6名参与者的步行、坐姿动作雷达回波做时频分析生成含微多普勒分量的语谱图，训练网络实现识别，实验验证了其有效性。
摘要：
本研究提出一种个人身份识别技术，将搭载双层卷积神经网络的机器学习应用于从运动目标人物雷达回波获取的语谱图图像。研究使用超宽带雷达系统测量了6名参与者的步行与坐姿动作。对雷达信号进行时频分析，生成包含与肢体运动相关的微多普勒分量的语谱图图像。使用带有个人标签的语谱图图像训练卷积神经网络，以实现基于雷达的个人身份识别。通过实验评估了个人身份识别准确率，验证了所提技术的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
