从雷达微多普勒签名的白化辅助学习,用于人类活动识别
Zahra Sadeghi Adl1, Fauzia Ahmad1
1Department of Electrical and Computer Engineering, Temple University, Philadelphia, PA 19122, USA.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
卷积神经网络 (CNN) 中的漂白层可以通过雷达提高人类活动的识别. 这种方法通过去关联雷达微多普勒签名来提高准确性,优于传统的批量规范化.
科学领域:
- 人工智能的人工智能
- 信号处理 信号处理
- 雷达技术 雷达技术的使用
背景情况:
- 深度学习,特别是CNN,被广泛用于用雷达识别人类活动 (HAR).
- 目前用于HAR的CNN模型经常使用批量规范化 (BN) 来进行训练优化和通用化.
- 雷达微多普勒信号是这些深度学习模型的关键输入.
研究的目的:
- 为基于雷达的HAR引入和评估白化辅助CNN模型.
- 为了证明用白化层取代批量正常化层的好处.
- 用雷达传感器提高人类活动的分类准确性.
主要方法:
- 在CNN模型中用白化层取代批量规范化 (BN) 层.
- 利用了美白的能力,以中心,规模,和脱相关的激活.
- 利用白化矩阵的旋转自由来与活动类对准隐性空间激活.
主要成果:
- 与基于BN的模型相比,白化辅助的CNN模型实现了更高的分类准确性.
- 漂白有效地与无关联的雷达微多普勒签名激活.
- 拟议的方法在真实世界活动数据上显示出显著的绩效增长.
结论:
- 白化辅助CNN为基于雷达的人类活动识别提供了更高的性能.
- 白化提供优势比批量规范化通过decorrelating特征.
- 这种方法对于使用雷达传感器改进HAR系统具有重大潜力.
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