基于SSA-ELM神经网络的室内可见光定位系统

贾科军; 牛振; 于凯; 张志聪; 彭铎; 曹明华

doi:10.13756/j.gtxyj.2025.230154

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基于SSA-ELM神经网络的室内可见光定位系统

光通信系统与网络技术 | 更新时间：2025-02-17

- 基于SSA-ELM神经网络的室内可见光定位系统
- Indoor Positioning System based on SSA-ELM Neural Network for Visible Light
- 光通信研究 2025年第1期
- 作者机构：
  
  兰州理工大学　计算机与通信学院，兰州　 730050
- 作者简介：
  
  贾科军，教授。E-mail：kjjia@lut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61875080);甘肃省科技计划资助项目(22JR5RA276;22JR5RA274;23YFGA0062;2022A-215);兰州理工大学博士科研启动经费资助项目(061903)
- DOI：10.13756/j.gtxyj.2025.230154
  中图分类号： O436
- 纸质出版日期：2025-02-10，
  
  收稿日期：2023-10-15，
  
  修回日期：2023-12-04，
- 稿件说明：
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贾科军，牛振，于凯，等. 基于SSA-ELM神经网络的室内可见光定位系统[J]. 光通信研究，2025(1): 230154.

Jia K J, Niu Z, Yu K, et al. Indoor Positioning System based on SSA-ELM Neural Network for Visible Light[J]. Study on Optical Communications, 2025(1): 230154.
贾科军，牛振，于凯，等. 基于SSA-ELM神经网络的室内可见光定位系统[J]. 光通信研究，2025(1): 230154. DOI： 10.13756/j.gtxyj.2025.230154.

Jia K J, Niu Z, Yu K, et al. Indoor Positioning System based on SSA-ELM Neural Network for Visible Light[J]. Study on Optical Communications, 2025(1): 230154. DOI： 10.13756/j.gtxyj.2025.230154.

摘要

【目的】

针对极限学习机（ELM）神经网络在室内可见光定位（VLP）中收敛不稳定，易陷入局部最优状态，导致定位精度降低的问题，文章引入了麻雀搜索算法（SSA）确定ELM神经网络的初始权值和阈值，提出了SSA-ELM神经网络算法。

【方法】

首先，采集定位区域内接收信号强度（RSS）与位置信息作为指纹数据；然后，训练SSA-ELM神经网络并得到预测模型，将测试集数据输入预测模型得到待测位置的定位结果；最后，设计了仿真实验和测试平台。

【结果】

仿真表明，在立体空间模型中0、0.3、0.6和0.9 m 4个接收高度，平均误差分别为1.73、1.86、2.18和3.47 cm，与反向传播（BP）、SSA-BP和ELM定位算法相比，SSA-ELM神经网络算法定位精度分别提高了83.55%、45.71%和26.26%，定位时间分别降低了36.48%、17.69%和6.61%。实验测试表明，文章所提SSA-ELM神经网络算法的平均定位误差为3.75 cm，比未优化的ELM神经网络定位精度提高了16.38%。

【结论】

SSA对ELM神经网络具有明显的优化作用，能够显著降低定位误差，减少定位时间。

Abstract

【Objective】

The Extreme Learning Machine (ELM) neural network algorithm in the traditional indoor Visible Light Positioning (VLP) system suffers from unstable convergence and a tendency to get stuck in local optimal states

resulting in decreased positioning accuracy. In this paper

a Sparrow Search Algorithm (SSA)-ELM neural network algorithm was proposed by introducing the SSA to determine the initial weights and thresholds of the ELM neural network.

【Methods】

Firstly

the Received Signal Strength (RSS) and location information within the targeted area are collected as fingerprint data. Subsequently

the SSA-ELM neural network is trained to obtain a prediction model

and the test set data is input into this model to derive the positioning result for the location under test. Finally

simulation experiments and a testing platform are designed.

【Results】

The simulation results show that at the four receiving heights of 0

0.3

0.6

and 0.9 m in the three-dimensional space model

the average errors are 1.73

1.86

2.18

and 3.47 cm

respectively. Compared with the Back Propagation (BP)

SSA-BP

and ELM positioning algorithms

the positioning accuracy of the SSA-ELM algorithm is improved by 83.55%

45.71%

and 26.26%

respectively

while the positioning time is reduced by 36.48%

17.69%

and 6.61%

respectively. Experimental tests have shown that the average positioning error of the SSA-ELM neural network algorithm proposed in the article is 3.75 cm

representing a 16.38% improvement in positioning accuracy compared to the unoptimized ELM neural network.

【Conclusion】

The SSA-ELM neural network algorithm proposed in this paper has an obvious optimization effect on the ELM neural network

which can significantly reduce the positioning error of the system and reduce the positioning time of the system.

关键词

Keywords

references

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