在微波雷达中识别人类活动的多尺度残余加权分类网络
Yukun Gao1, Lin Cao1,2, Zongmin Zhao1,2
1School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
这项研究引入了一种新的多尺度残量加权分类网络 (MRW-CN),用于基于雷达的人类活动识别. 该模型达到96.9%的准确性,克服了智能家居和医疗保健应用程序中有限的标记数据的挑战.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 使用雷达传感器识别人类活动 (HAR) 对医疗保健和智能家居至关重要.
- 标记大型雷达数据集是耗时的,并且阻碍了模型性能.
- 由于标记数据不足,现有的模型在分类准确性方面扎.
研究的目的:
- 提出一个新的多尺度残量加权分类网络 (MRW-CN),以实现高效的HAR.
- 为了应对雷达中有限的标记数据的挑战,HAR.
- 为了提高基于雷达的活动识别的分类准确性.
主要方法:
- 使用多尺度残余加权 (MRW) 图像编码器与对比学习进行特征提取.
- 采用大,中,小规模的剩余网络,用于全球,纹理和语义信息.
- 包含了一个时间通道权重机制,用于增强特征提取.
- 预先训练了MRW编码器,结了参数,并微调了带有有限标记数据的分类器.
主要成果:
- 在对八种危险活动的新建数据集上实现了96.9%的分类准确性.
- 在基于雷达的人类活动识别方面展示了最先进的性能.
- 废弃性研究证实了多尺度内核和时间道权重的有效性.
结论:
- 拟议的MRW-CN模型有效地解决了雷达HAR标记数据不足的局限性.
- 多尺度方法和时间道权重显著提高了特征表示和分类准确度.
- 这种方法为在现实应用中可靠地识别人类活动提供了一个有希望的解决方案.
相关概念视频
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01:30Classification of Signals
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A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...