Improved Weighted K Nearest Neighbor Algorithm for Indoor Visible Light Fingerprint Positioning System

被引：2

作者：

Liang Zhehao ^{[1
]}

Shi Lei ^{[1
]}

Tang Jie ^{[1
]}

Li Jiahao ^{[1
]}

Cao Yuexiang ^{[1
]}

机构：

[1] Air Force Engn Univ, Coll Informat & Nav, Aviat Commun Teaching & Res Off, Xian 710077, Shaanxi, Peoples R China

来源：

LASER & OPTOELECTRONICS PROGRESS | 2022年 / 59卷 / 17期

关键词：

optical communications; visible light positioning; indoor positioning; fingerprint location; weighted K nearest; neighbor algorithm;

D O I：

10.3788/LOP202259.1706005

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Aiming at the problem that the Euclidean distance in the weighted K nearest neighbor (WKNN) algorithm can not effectively represent the actual distance relationship between measurement points in the indoor visible light fingerprint positioning system, an improved WKNN algorithm based on weighted Euclidean distance measurement is proposed in this paper. The algorithm assigns different weighting coefficients to different signal strength differences according to the attenuation characteristics of the received signal strength varying with the actual distance. The simulation results show that under the same environmental conditions, compared with the WKNN algorithm using European distance measurement and Manhattan distance measurement, the average positioning error of the improved algorithm is reduced by 37. 5% and 34. 3%, respectively.

引用

页数：5

共 23 条

[1] Accurate Visible Light Positioning Using Multiple-Photodiode Receiver and Machine Learning [J].