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FMCW雷达基于不对称的卷积剩余块识别人类行动

Yuan Zhang¹, Haotian Tang¹, Ye Wu²

¹School of Information Science and Technology, North China University of Technology, Beijing 100144, China.

Sensors (Basel, Switzerland)

|July 27, 2024

概括

这项研究引入了一种使用频率调制连续波 (FMCW) 雷达和非对称卷积残余网络的新型人类行动识别方法. 该方法实现了高精度,即使在复杂的环境和有噪音的数据.

科学领域:

雷达信号处理雷达信号处理
机器学习机器学习
人类行动识别人类行动识别

背景情况:

基于光学和红外视频的人类行为识别受环境因素和复杂的特征提取限制.
传统的方法在具有挑战性的条件下与特征提取作斗争.

研究的目的:

提出一种使用频率调制连续波 (FMCW) 雷达的先进人类行动识别方法.
克服复杂环境和特征提取现有方法的局限性.

主要方法:

利用FMCW雷达提取人类行动的微多普勒时域光谱图.
开发了一个不对称的卷积残余网络 (ResNet18变体),结合了不对称的卷积和Mish激活.
集成了一个改进的卷积块注意力模块 (ICBAM),以增强特征学习和数据理解.

主要成果:

在复杂场景中实现了98.28%的高动作识别准确度.
与经典的深度学习方法相比,表现出优越的性能.
显著改善了具有类似微多普勒特征的动作识别,并显示出出色的防噪性能.

结论:

拟议的FMCW基于雷达的方法为人类行动识别提供了强大而准确的解决方案.
与ICBAM一起的非对称卷积残余网络有效地提高了特征学习和识别精度.
这种方法为在多样化和具有挑战性的环境中可靠地识别人类行为提供了有希望的替代方案.

关键词:

在FMCW雷达.行动的认可行动的认可不对称的卷积卷积.注意力机制注意力机制深度学习是一种深度学习.微多普勒的微型多普勒.

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