Indoor visible light positioning based on dual ELM considering receiver orientation

DOU Daofei; ZHANG Suyang

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Indoor visible light positioning based on dual ELM considering receiver orientation

Optical Sensing and Measurement | 更新时间：2026-07-23

- Indoor visible light positioning based on dual ELM considering receiver orientation
- OPTICAL TECHNIQUE Vol. 52, Issue 4, Pages: 473-480(2026)
- 作者机构：
  
  1.中国铁道科学研究院集团有限公司通信信号研究所，北京 100081
  2.国家铁路智能运输系统工程技术研究中心，北京 100081
  3.国铁通信信号（北京）有限公司，北京 100081
  4.通信信号基础设施智能运维铁路行业工程研究中心，北京 100081
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP394.1;TH691.9
- Received：10 April 2026，
  
  Revised：2026-05-14，
  
  Accepted：20 May 2026，
  
  Online First：23 July 2026，
  
  Published：20 July 2026
- 稿件说明：
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窦道飞,张素阳.考虑接收机姿态的双ELM室内可见光定位方法[J].光学技术,2026,52(04):473-480.

DOU Daofei,ZHANG Suyang.Indoor visible light positioning based on dual ELM considering receiver orientation[J].OPTICAL TECHNIQUE,2026,52(04):473-480.
窦道飞,张素阳.考虑接收机姿态的双ELM室内可见光定位方法[J].光学技术,2026,52(04):473-480. DOI：

DOU Daofei,ZHANG Suyang.Indoor visible light positioning based on dual ELM considering receiver orientation[J].OPTICAL TECHNIQUE,2026,52(04):473-480. DOI：

摘要

针对接收机姿态动态变化引发接收信号特征畸变，导致室内可见光定位精度下降的问题，提出一种全局ELM（极限学习机）粗略定位与局部ELM精确定位相结合的室内可见光定位方法。首先，将接收信号强度与接收机姿态信息作为联合指纹特征，建立指纹特征与物理位置之间的映射关系，离线构建全局指纹数据库；然后，利用全局指纹数据库训练全局ELM模型，粗略确定待测接收机位置；接着，筛选指纹样本，自适应构建局部指纹数据库，并结合接收机实时姿态信息，对局部指纹数据进行方向一致性校正与更新；最后，利用局部指纹数据库训练局部ELM模型，实现在线精确定位。实验结果表明，所提方法在维持较高计算效率的同时，增强了接收机姿态变化的适应能力，可为室内可见光定位提供一种定位精度高、鲁棒性强的参考方案。

Abstract

To solve the problem of accuracy decline of indoor visible light positioning resulting from the distortion of received signal characteristics with dynamic changes of receiver orientation

a practical approach to indoor visible light positioning has been proposed

which combines the global ELM （extreme learning machine） for approximate positioning with local ELM for precise positioning. Firstly

received signal strength and receiver orientation information are used as joint fingerprint features to establish the mapping relationship between fingerprint features and physical position

and a global fingerprint database is constructed offline. Then

the global fingerprint database is adopted to train the global ELM model to roughly determine the position of the receiver under test. Next

fingerprint samples are selected to adaptively construct the local fingerprint database

which is calibrated and updated for direction consistency using the real-time information of receiver orientation. Finally

the local fingerprint database is utilized to train the local ELM model to achieve the online precise position information. Experimental results demonstrate that the proposed method not only maintains high computational efficiency but also enhances the adaptability to the variation of receiver orientation

which can provide a highly accurate and strongly robust reference solution for indoor visible light positioning.

关键词

Keywords

references

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