一种融合时频特征的UWB非视距识别方法

何成文; 肖祖贵; 刘佩; 李昊成; 孙昊

doi:10.11959/j.issn.2096-3750.WLW26064

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一种融合时频特征的UWB非视距识别方法

更新时间：2026-09-29

- 一种融合时频特征的UWB非视距识别方法
- A uwb non-line-of-sight identification method integrating time-frequency features
- 物联网学报 2026年
- 作者机构：
  
  1.湖南工商大学人工智能与机器人学院，湖南长沙 410205
  2.湖南工商大学智能工程与智能制造学院，湖南长沙 410205
  3.湖南工商大学微电子与物理学院，湖南长沙 410205
  4.湖南工商大学计算机学院，湖南长沙 410205
- 作者简介：
  
  肖祖贵，2113778989@qq.com
- 基金信息：
  
  湖南省自然科学基金（部门联合）(2025JJ80006);北斗高精度卫星导航与位置服务湖南省工程研究中心开放课题(KFKT2025-N004);湖南省教育厅优秀青年项目(22B0637)
- DOI：10.11959/j.issn.2096-3750.WLW26064
  中图分类号： TN925
- 收稿：2026-05-21，
  
  修回：2026-09-02，
  
  录用：2026-09-07，
- 稿件说明：
移动端阅览
何成文, 肖祖贵, 刘佩, 等. 一种融合时频特征的UWB非视距识别方法[J/OL]. 物联网学报, 2026.

HE Chengwen, XIAO Zugui, LIU Pei, et al. A uwb non-line-of-sight identification method integrating time-frequency features[J/OL]. Chinese Journal on Internet of Things, 2026.
何成文, 肖祖贵, 刘佩, 等. 一种融合时频特征的UWB非视距识别方法[J/OL]. 物联网学报, 2026. DOI： 10.11959/j.issn.2096-3750.WLW26064.

HE Chengwen, XIAO Zugui, LIU Pei, et al. A uwb non-line-of-sight identification method integrating time-frequency features[J/OL]. Chinese Journal on Internet of Things, 2026. DOI： 10.11959/j.issn.2096-3750.WLW26064.

摘要

针对复杂室内场景中超宽带（UWB）定位系统受非视距（NLOS）传播影响，造成测距精度不足和定位性能下降的问题，提出了一种物理特征引导的自适应融合网络，该网络以信道冲激响应（CIR）为处理对象，用时域分析和频域分析的结合方式处理信号：先对CIR信号做快速傅里叶变换提取频域特征，同时提取第一径能量占比、峰值对比和多径扩展量等物理特征，然后利用物理特征引导的双向门控循环单元（BiGRU）和通道注意力机制，完成时频域特征的动态融合，以此提高模型在复杂环境里分辨NLOS信号的能力。实验结果表明，PGAF-Net在公开数据集上的综合性能都优于对比算法，在NLOS信号识别上有着较好的准确率与稳定性。

Abstract

Aiming at the problem that the ultra-wideband (UWB) positioning system is affected by non-line-of-sight (NLOS) propagation in complex indoor scenes

resulting in insufficient ranging accuracy and degraded positioning performance

an adaptive fusion network guided by physical features was proposed. Channel impu-lse response (CIR) was adopted as the processing object

and a combination of time-domain and frequency-domain analyses was employed for signal processing : First

the fast Fourier transform was performed on the CIR signal to extract the frequency domain features

and the physical features such as the first-path energy ratio

peak comparison

and multipath expansion were extracted. Then

the dynamic fusion of time-frequency domain features is completed by using physical feature guided bidirectional gated recurrent unit ( BiGRU ) and channel attention mechanism

thereby improving the model’s capability to distinguish NLOS signals in complex environments. The experimental results show that the comprehensive performance of PGAF-Net on public data sets is better than that of comparison algorithms

and it has better accuracy and stability in NLOS signal recognition.

关键词

Keywords

references

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