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arXiv 2104.00217eess.SP
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基于多天线系统的雷达人体运动分类

Radar Human Motion Classification Using Multi-Antenna System

Patrick A. Schooley, Syed A. Hamza

摘要:

本文考虑室内雷达系统的人类活动分类。人的运动产生非平稳雷达回波,代表多普勒和微多普勒信号。微多普勒信号的时频(TF)分析可以通过精确地揭示各种运动身体部位的速度分量来辨别运动的细微变化。我们考虑使用TF为基础的机器学习方法,利用时间和空间自由度的活动监测雷达。所提出的方法捕捉不同的人的运动表示更生动地在联合变量数据域实现通过波束成形在接收器。使用四个接收天线在77 GHz下使用实时测量收集雷达数据,随后通过机器学习算法分析微多普勒特征,用于人类行走运动的分类。我们提出的多天线方法在分离和分类两个密切行走的人在相反的方向移动的性能。

英文摘要:

This paper considers human activity classification for an indoor radar system. Human motions generate nonstationary radar returns which represent Doppler and micro-Doppler signals. The time-frequency (TF) analysis of micro-Doppler signals can discern subtle variations on the motion by precisely revealing velocity components of various moving body parts. We consider radar for activity monitoring using TF-based machine learning approach exploiting both temporal and spatial degrees of freedom. The proposed approach captures different human motion representations more vividly in joint-variable data domains achieved through beamforming at the receiver. The radar data is collected using real time measurements at 77 GHz using four receive antennas, and subsequently micro-Doppler signatures are analyzed through machine learning algorithm for classifications of human walking motions. We present the performance of the proposed multi antenna approach in separating and classifying two closely walking persons moving in opposite directions.

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。

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