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相关实验视频

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一个多任务网络,用于人数计数,运动识别和定位,使用透墙雷达.

Junyu Lin1, Jun Hu1, Zhiyuan Xie1

  • 1School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括

本研究介绍了一种用于透墙雷达 (TWR) 系统的新型多任务网络. 该网络准确地同时进行人数计数,动作识别和定位,增强安全和救援行动.

关键词:
多普勒签名是多普勒的签名.在本地化,本地化.动作识别 运动识别 运动识别人们在计数,人们在计数.剩余注意力网络 剩余注意力网络通过墙壁的雷达.

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科学领域:

  • 雷达系统工程 雷达系统工程
  • 人工智能在安全方面的应用
  • 信号处理 信号处理

背景情况:

  • 透墙雷达 (TWR) 使用低频电磁波进行穿透检测,这对于公共安全,反恐和灾难救援至关重要.
  • 目前的TWR研究通常集中在检测或计数等单一任务上,限制了全面的情境意识.
  • 实际的TWR应用需要同时计数人员,动作识别和精确的定位.

研究的目的:

  • 开发和验证一个能够同时执行人数计数,动作识别和本地化使用TWR数据的多任务网络.
  • 处理来自1D雷达信号的范围-时间-多普勒 (RTD) 光谱,以进行全面的人类活动分析.
  • 通过提供集成的人员监控能力来解决单任务TWR系统的局限性.

主要方法:

  • 一个采用卷积层和注意力模块的多任务深度学习网络被设计用于处理RTD频谱.
  • 产生了信心矩阵,将人数,运动类别和位置信息编码为标签.
  • 制定了一个总和的个别任务损失函数,将本地化问题转换为多标签分类任务.

主要成果:

  • 多任务网络在10,032个样本的测试组中,在人数计数方面达到96.94%的准确率,在运动识别方面达到96.03%.
  • 该系统显示,通过24厘米厚的墙定位的平均距离误差为0.12米.
  • 拟议的方法有效地提取深度特征,用于同时进行多任务TWR分析.

结论:

  • 开发的多任务网络为集成的人员计数,动作识别和局部化提供了强大而准确的解决方案.
  • 这种方法显著提高了TWR系统的功能,以提高公共安全和应急响应.
  • 这些发现突显了深度学习和注意力机制在复杂的雷达信号处理中对现实世界的应用的潜力.