基于FMCW雷达传感器的人类活动识别方法,具有多域特征注意力融合网络
Lin Cao1,2, Song Liang1,2, Zongmin Zhao1,2
1The Key Laboratory of Information and Communication Systems, Ministry of Information Industry, Beijing Information Science and Technology University, Beijing 100101, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的基于雷达的人类活动识别 (HAR) 方法,使用多域特征注意力融合网络 (MFAFN). 先进的模型实现了高精度,大大提高了复杂的人类运动的识别.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 信号处理 信号处理
背景情况:
- 人类活动识别 (HAR) 对各种应用至关重要.
- 使用雷达传感器的现有 HAR 方法通常依赖于单个特征,限制性能.
- 频调制连续波 (FMCW) 雷达为非侵入性HAR提供了潜在的潜力.
研究的目的:
- 为FMCW雷达传感器提出一个先进的HAR方法.
- 克服目前HAR技术中单一特征依赖的局限性.
- 提高人类活动分类的准确性和稳定性.
主要方法:
- 开发一个多域特征注意力融合网络 (MFAFN).
- 时间多普勒 (TD) 和时间范围 (TR) 地图的融合,用于全面的活动表示.
- 实现多功能注意力融合模块 (MAFM) 与频道注意力.
- 对可混样本应用多重分类焦点损失 (MFL) 函数.
主要成果:
- 在格拉斯哥大学数据集上实现了97.58%的识别准确度.
- 与现有 HAR 方法相比,其表现有 0.9-5.5% 的性能改善.
- 在分类可混的人类活动方面表现出显著的改善,高达18.33%.
结论:
- 拟议的MFAFN方法提供了使用FMCW雷达对HAR的优越方法.
- 多域特征融合和注意力机制增强了活动识别能力.
- 该方法有效地解决了分类类似人类活动的挑战.
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