基于BPNN-MLR的室内可见光定位算法

秦岭; 刘哲; 王凤英; 郭瑛; 徐艳红; 胡晓莉

doi:10.13756/j.gtxyj.2022.02.002

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基于BPNN-MLR的室内可见光定位算法

光通信系统与网络技术 | 更新时间：2022-06-15

- 基于BPNN-MLR的室内可见光定位算法
- Indoor Visible Light Positioning Algorithm based on BPNN-MLR
- 光通信研究 2022年第2期页码：7-11
- 作者机构：
  
  内蒙古科技大学　信息工程学院，内蒙古　包头　014010
- 作者简介：
  
  秦岭(1979-)，女，内蒙古包头人。教授，博士，主要研究方向为可见光通信和室内定位。
  胡晓莉，副教授。E-mail：huxiaoli@imust.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61961033);内蒙古自治区自然科学基金资助项目(2019MS06021;2019LH06005);内蒙古自治区高等学校“青年科技英才支持计划”资助项目(NJYT-19-A15)
- DOI：10.13756/j.gtxyj.2022.02.002
  中图分类号： TN929.1
- 纸质出版日期：2022-04-10，
  
  收稿日期：2021-04-15，
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秦岭, 刘哲, 王凤英, 等. 基于BPNN-MLR的室内可见光定位算法[J]. 光通信研究, 2022,(2):7-11.

QIN Ling, LIU Zhe, WANG Feng-ying, et al. Indoor Visible Light Positioning Algorithm based on BPNN-MLR[J]. Study on Optical Communications, 2022,(2):7-11.
秦岭, 刘哲, 王凤英, 等. 基于BPNN-MLR的室内可见光定位算法[J]. 光通信研究, 2022,(2):7-11. DOI： 10.13756/j.gtxyj.2022.02.002.

QIN Ling, LIU Zhe, WANG Feng-ying, et al. Indoor Visible Light Positioning Algorithm based on BPNN-MLR[J]. Study on Optical Communications, 2022,(2):7-11. DOI： 10.13756/j.gtxyj.2022.02.002.

摘要

针对传统位置指纹算法存在定位精度低和计算复杂度高等问题，文章提出了一种基于反向传播神经网络(BPNN)和多元线性回归(MLR)的单发光二极管(LED)室内定位算法。首先，利用3个水平光电探测器(PD)作为接收器接收光功率，待测点位于接收器的中心；然后根据接收到的光功率向量，利用BPNN确定待测点粗略的位置范围；最后以该位置范围作为约束条件，利用MLR对待测点的位置进行更精确地定位。实验结果表明，在2.0 m × 2.0 m × 2.5 m的室内空间中，该算法的平均定位误差为5.04 cm，平均定位时间为0.002 83 s。与传统的位置指纹算法相比，该算法的平均定位精度提高了41.53%，平均定位时间减少了56.60%，在较低计算复杂度的前提下实现了更精确的定位。

Abstract

Aiming at the problems of low positioning accuracy and high computational complexity of the traditional location fingerprint algorithm

this paper proposes a single Light Emitting Diode (LED) indoor positioning algorithm based on Back Propagation Neural Network and Multiple Linear Regression (MLR). First

three horizontal PhotoDetectors (PD) are used at the receiver side to receive optical power

and the point to be measured is located at the center of the receivers. Then

according to the received optical power vector

the BP neural network is used to obtain the coarse location range of the point to be measured. Finally

the location range is used as a constraint

and multiple linear regression is used to achieve more accurate positioning of the point. The experimental results demonstrate that in an indoor space of 2.0 m × 2.0 m × 2.5 m

the average positioning error of the algorithm is 5.04 cm

and the average positioning time is 0.002 83 s. Compared with the traditional location fingerprint algorithm

the average positioning accuracy of the algorithm is improved by 41.53%

and the average positioning time is reduced by 56.60%

which achieves more accurate positioning under the requirement of lower computational complexity.

关键词

可见光通信室内定位接收光功率反向传播神经网络多元线性回归

Keywords

visible light communicationindoor positioningreceived optical powerBPNNMLR

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