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雷达信号处理及其对深度学习驱动的人类活动识别的影响

Fahad Ayaz¹, Basim Alhumaily¹, Sajjad Hussain¹

¹James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)

|February 13, 2025

概括

雷达技术与卷积神经网络 (CNN) 结合,提高了人类活动识别 (HAR). 带有STFT预处理的MobileNetV2实现了96.30%的准确性,平衡实时应用程序的效率和性能.

科学领域:

雷达信号处理雷达信号处理
机器学习机器学习
计算机视觉计算机视觉

背景情况:

人类活动识别 (HAR) 对智能安全和医疗保健至关重要.
雷达技术为HAR提供了一种保护隐私的替代方案.
将先进的信号处理与深度学习相结合是改善HAR的关键.

研究的目的:

调查CNN与雷达信号处理的整合,以提高HAR.
评估不同的雷达地图生成技术和CNN架构.
为了确定实时,资源受限的HAR应用程序的最佳配置.

主要方法:

使用了三种2D雷达处理技术:范围-FFT时间范围地图,STFT时间多普勒地图和SPWVD地图.
评估了四个CNN架构:VGG-16,VGG-19,ResNet-50和MobileNetV2. 这四个架构都在进行评估.
分析了12个CNN和预处理配置,以获得准确性和计算效率.

主要成果:

带有STFT预处理的MobileNetV2实现了96.30%的准确性.
这种配置表现出高计算效率,推断时间为2.57ms.
谱图生成时间为220毫秒,适合实时处理.

结论:

雷达生成的地图作为HAR的有效视觉数据,确保隐私.
该研究强调了预处理复杂性和识别精度之间的权衡.
最佳的配置,如STFT的MobileNetV2,使得基于雷达的HAR在边缘计算和AR中得到更广泛的应用.

关键词:

计算成本是计算成本.深度学习是一种深度学习.人类活动分类人类活动分类雷达领域表示雷达领域表示转移学习转移学习

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