强大的手势识别使用可变形双流融合网络基于CNN-TCN的FMCW雷达
Meiyi Zhu1, Chaoyi Zhang1,2, Jianquan Wang1,2
1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
本研究引入了一种新的基于雷达的手势识别方法,使用可变形双流融合网络 (DDF-CT). 这种方法即使在新环境中也能达到高精度,克服了雷达信号处理方面的挑战.
科学领域:
- 计算机科学 计算机科学
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 使用频率调制连续波 (FMCW) 雷达的手势识别 (HGR) 面临由于个体变化和环境因素的挑战.
- 现有的HGR方法与雷达信号固有的模糊性作斗争.
研究的目的:
- 提出一种基于CNN-TCN (DDF-CT) 的新型可变形双流融合网络,用于使用FMCW雷达进行强大的HGR.
- 通过解决信号变化和模糊性来提高HGR的准确性和一致性.
主要方法:
- 从雷达信号中提取射程,多普勒和角度信息,使用快速里埃变换来创建射程-时间 (RT) 和射程-角度 (RA) 地图.
- 通过临时组织RA地图来捕捉目标特征和时间特征,生成范围-角度-地图序列 (RAMS).
- 在DDF-CT网络中整合可变形卷积和框架间注意力机制,以改善空间特征提取和时间关系学习.
主要成果:
- 拟议的DDF-CT方法在手势识别方面实现了98.61%的高精度.
- 该方法在新环境中表现出强大的性能,保持了97.22%的准确性.
- 实验结果表明,它们与现有的高温回流方法相比,具有显著的优势.
结论:
- DDF-CT网络通过FMCW雷达有效地应对HGR的挑战,提供更高的准确性和稳定性.
- 可变形卷曲和注意力机制的融合是增强空间和时间特征学习的关键.
- 这种方法代表了基于雷达的手势识别技术的重大进步.
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