Open AccessResearch on Indoor Visible Light Positioning Algorithm Based on K-means Clustering
Author(s) -
Rui Zhang,
Yerong Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1792/1/012076
Subject(s) - cluster analysis , rss , computer science , cluster (spacecraft) , enhanced data rates for gsm evolution , algorithm , point (geometry) , precise point positioning , artificial intelligence , computer vision , mathematics , global positioning system , telecommunications , geometry , operating system , gnss applications , programming language
In order to improve the indoor positioning accuracy and reduce the impact of a reflected light on the positioning performance, based on the analysis of the existing RSS positioning algorithm, an improved positioning algorithm based on K-means clustering is proposed. According to the indoor visible light communication system model and optical power distribution, 10*10 receiving points are uniformly selected on the receiving surface of 5m*5m. Through the collection of the received optical power of each receiving point, the K-means clustering algorithm is used to classify 100 receiving points: the first cluster of points is less affected by a reflected light, and the second cluster of points is greatly affected by a reflected light. The second cluster of receiving points is weighted after classifying, and the simulation results show that the algorithm significantly reduces the room edge positioning error, and the positioning performance of the entire room is improved by 28%.