应用时间卷积网络算法与UWB室内定位的频道注意模块融合
Liuhui He1, Zengzeng Lian2, M Amparo Núñez-Andrés3
1School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China.
Scientific reports
|January 27, 2026
概括
本研究介绍了一种具有通道注意模块 (TCN-CAM) 的新型时间卷积网络,以提高超宽带 (UWB) 室内定位精度,特别是在具有挑战性的非视线环境中.
科学领域:
- 机器人和自动化 机器人和自动化
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 超宽带 (UWB) 技术对于室内定位很有价值,但在非视线 (NLOS) 条件下,尤其是动态的人类运动时,其准确性会下降.
- 现有的神经网络方法可能会在复杂的时间序列定位数据中与远程依赖性和特征突出性作斗争.
研究的目的:
- 开发和评估一种具有通道注意模块 (TCN-CAM) 的新型时间卷积网络,以提高UWB室内定位性能.
- 通过先进的深度学习技术,解决动态NLOS环境中UWB准确性的局限性.
主要方法:
- 提出了一个TCN-CAM模型,整合因果和扩展卷积,以捕捉远程时间依赖性和减轻消失梯度.
- 整合了通道注意模块 (CAM),以适应性地关注复杂定位场景中的突出特征.
- 进行模拟和实地实验以验证TCN-CAM算法的有效性.
主要成果:
- TCN-CAM算法实现了高定位精度和稳定性,平均误差低至3.32厘米.
- 与LSTM-AM (76.12%),CNN-CAM (25.06%) 和传统TCN (19.42%) 相比,在定位准确度方面取得了显著的改进.
结论:
- 拟议的TCN-CAM方法大大提高了基于UWB的室内定位系统的稳定性和性能.
- 在具有挑战性的NLOS和动态环境中,TCN-CAM为准确和稳定的UWB定位提供了卓越的解决方案.
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