PosFormer:可通过全球本地特征融合网络进行通用化的室内定位
IEEE transactions on neural networks and learning systems
|January 13, 2026
概括
PosFormer是一个新的深度学习模型,通过融合变压器和CNN模块来提高室内定位准确度,使用超宽带 (UWB) 信号. 它克服了多路径挑战,在复杂的环境中实现了卓越的性能.
科学领域:
- 机器人和自动化 机器人和自动化
- 无线通信和网络无线通信和网络.
- 信号处理 信号处理
背景情况:
- 超宽带 (UWB) 技术提供了高精度的室内定位,但在多路径非视线 (NLOS) 环境中难以实现.
- 现有的方法,包括基于几何和深度学习的方法,在准确捕获UWB信号传播特征方面存在局限性.
- 挑战包括传统方法中的距离估计过高以及当前深度学习模型中功能提取不足.
研究的目的:
- 开发一种先进的深度学习模型,用于使用UWB信号的高精度室内定位.
- 在复杂,多路径丰富的环境中解决现有方法的局限性.
- 提高UWB本地化系统的稳定性和部署效率.
主要方法:
- 拟议的PosFormer是一个双融合网络,结合了变压器和卷积神经网络 (CNN) 模块来处理道脉冲响应 (CIR).
- 集成的多路径物理信息来增强特征提取.
- 引入了CIR多样性的非相邻子集 (NAASs) 方案和跨环境部署的轻量级转移学习 (TL) 框架.
- 利用公共工业数据集进行广泛的实验验证.
主要成果:
- PosFormer实现了17.64厘米的平均绝对误差 (MAE),显著超过了基线模型 (CNN,LSTM,变压器).
- 在具有挑战性的工业大厅环境中,TL框架使预训练模型能够以仅20%的指纹数据达到35.92厘米的精度.
- 在从UWB CIR中提取全球和本地多尺度特征方面表现出卓越的性能.
结论:
- 拟议的PosFormer模型有效地解决了NLOS环境中的UWB室内定位挑战.
- 双融合网络架构和TL框架提高了数据的准确性,稳定性和效率.
- PosFormer显示了对现实世界UWB本地化应用的显著实用价值,特别是在工业环境中.
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