一种行人遮挡下的UWB非视距传播识别方法

吴彤; 李业深; 黄镇煌; 张煜; 张万乐; 熊轲

doi:10.11959/j.issn.2096-3750.2023.00348

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一种行人遮挡下的UWB非视距传播识别方法

理论与技术 | 更新时间：2024-08-16

- 一种行人遮挡下的UWB非视距传播识别方法
- A UWB NLOS identification method under pedestrian occlusion
- 物联网学报 2023年7卷第4期页码：63-71
- 作者机构：
  
  1. 北京交通大学高速铁路网络管理教育部工程研究中心，北京 100044
  2. 北京交通大学计算机与信息技术学院，北京 100044
  3. 国网能源研究院有限公司，北京 102209
- 作者简介：
  
  [ "吴彤（1997- ），女，北京交通大学计算机与信息技术学院硕士生，主要研究方向为室内定位" ]
  [ "李业深（2000- ），男，北京交通大学计算机与信息技术学院硕士生，主要研究方向为人工智能、高铁无线通信等" ]
  [ "黄镇煌（1998- ），男，北京交通大学计算机与信息技术学院硕士生，主要研究方向为网络智能与移动计算、联邦学习" ]
  [ "张煜（1983- ），男，博士，国网能源研究院有限公司高级研究员，主要研究方向为边缘计算、无线协作网络和能源互联网等" ]
  [ "张万乐（1998- ），男，北京交通大学计算机与信息技术学院博士生，主要研究方向为机器学习、移动和云计算、无线通信网络" ]
  [ "熊轲（1981- ），男，博士，北京交通大学计算机与信息技术学院教授、副院长，主要研究方向为人工智能+5G/6G网络、无线大数据分析与处理、AI 赋能的移动网络优化设计、绿色智慧物联网、网络大数据分析、雾计算/边缘计算、室内定位、基于无线大数据的人体姿态识别等" ]
- 基金信息：
  
  中央高校基本科研业务费资助项目(2022JBGP003);国家自然科学基金项目(62071033);国家重点研发计划;The National Key Research and Development Program of China(2020YFB1806903)
- DOI：10.11959/j.issn.2096-3750.2023.00348
  中图分类号： TN91
- 纸质出版日期：2023-12-20，
  
  网络出版日期：2023-12，
- 稿件说明：
移动端阅览
吴彤, 李业深, 黄镇煌, 等. 一种行人遮挡下的UWB非视距传播识别方法[J]. 物联网学报, 2023,7(4):63-71.

TONG WU, YESHEN LI, ZHENHUANG HUANG, et al. A UWB NLOS identification method under pedestrian occlusion. [J]. Chinese journal on internet of things, 2023, 7(4): 63-71.
吴彤, 李业深, 黄镇煌, 等. 一种行人遮挡下的UWB非视距传播识别方法[J]. 物联网学报, 2023,7(4):63-71. DOI： 10.11959/j.issn.2096-3750.2023.00348.

TONG WU, YESHEN LI, ZHENHUANG HUANG, et al. A UWB NLOS identification method under pedestrian occlusion. [J]. Chinese journal on internet of things, 2023, 7(4): 63-71. DOI： 10.11959/j.issn.2096-3750.2023.00348.

摘要

超宽带（UWB

ultrawideband）技术带宽大、抗干扰能力强、多径分辨率高，是室内定位的热点技术。然而由于室内环境复杂，UWB 信号传播不可避免会受到遮挡，产生非视距（NLOS

non-line-of-sight）传播，极大降低了UWB定位的精度。因此，准确识别出NLOS数据，将其进行剔除或矫正，对缓解定位精度下降问题有重要的作用。现有NLOS识别工作多数聚焦于墙体等建筑结构遮挡的场景，行人遮挡的场景需要进一步讨论。由于人体遮挡对信号的影响复杂且不可忽略，针对行人遮挡下的UWB非视距传播识别问题进行研究。综合比较多种机器学习方法和信号特征组合，提出了一种基于第一路径信号功率、总接收信号功率和测量距离三维特征的随机森林方法，使用较少维度的特征达到了良好的NLOS识别效果。基于不同实测数据的实验结果表明，采用所提三维特征的随机森林方法在3组不同数据集上的NLOS识别准确率分别达到了99.05%、99.32%和98.81%。

Abstract

Ultrawideband (UWB) is a hot technology for indoor positioning with large bandwidth

strong anti-interference ability

and high multipath resolution capacity.However

due to the complex indoor environment

UWB signal propagation will inevitably be blocked

resulting in non-line-of-sight (NLOS) propagation

which greatly reduces the accuracy of UWB positioning.Therefore

identifying NLOS signals accurately and discarding or correcting them are important to alleviate the problem of the decline in positioning accuracy.The majority of present NLOS identification work focuses on scenes with building structures such as walls.Further discussion is needed for scenes obscured by pedestrians.Since the impact of human obstacles on the signals is more complex and cannot be ignored

the NLOS identification under pedestrian occlusion was studied.By comparing a variety of machine learning methods and signal feature combinations

the random forest method based on the three-dimensional features of the first path signal power

the received signal power

and the measured distance was proposed.These features with fewer dimensions and easy extraction were used to achieve a high identification percentage for NLOS.The experimental results based on the measured data of different devices show that the NLOS identification accuracy based on the proposed method reaches 99.05%

99.32% and 98.81% respectively.

关键词

UWB室内定位非视距识别随机森林

Keywords

UWBindoor positioningNLOS identificationrandom forest

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