Delineating Regional BES–ELM Neural Networks for Studying Indoor Visible Light Positioning

• Published in: Photonics Vol. 11; no. 10; p. 910
• Main Authors: Zhang, Jiaming, Ke, Xizheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2024
• Subjects: Accuracy; Algorithms; Analysis; Back propagation networks; Bald eagle; BES–ELM neural network; Cluster analysis; Clustering; Communication; Construction; Datasets; Error analysis; Error correction; Indoor environments; Light; Light-emitting diodes; Machine learning; Neural networks; Optimization; region division; Signal strength; Vector quantization; visible light optical communication; visible light positioning; Wireless networks
• ISSN: 2304-6732; 2304-6732
• DOI: 10.3390/photonics11100910

This paper introduces a single LED and four photodetectors (PDs) as a visible light system structure and collects the received signal strength values and corresponding physical coordinates at the PD receiving end, establishing a comprehensive dataset. The K-means clustering algorithm is employed to separate the room into center and boundary areas through the fingerprint database. The bald eagle search (BES) algorithm is employed to optimize the initial parameters, specifically the weights and thresholds, in the extreme learning machine (ELM) neural network, and the BES–ELM indoor positioning model is established by region to improve positioning accuracy. Due to the impact exerted by the ambient environment, there are fluctuations in the positioning accuracy of the center and edge regions, and the positioning of the edge region needs to be further improved. To address this, it is proposed to use the enhanced weighted K-nearest neighbor (EWKNN) algorithm based on the BES–ELM neural network to correct the prediction points with higher-than-average positioning errors, achieving precise edge positioning. The simulation demonstrates that within an indoor space measuring 5 m × 5 m × 3 m, the algorithm achieves an average positioning error of 2.93 cm, and the positioning accuracy is improved by 86.07% relative to conventional BP neural networks.