Indoor Real-Time 3-D Visible Light Positioning System Using Fingerprinting and Extreme Learning Machine

Abstract

Photodiode-based (PD-based) visible light positioning (VLP) has become a research focus of indoor positioning technology, while the existing VLP models rarely consider the anti-interference and positioning time of that. In this paper, indoor real-time three-dimensional visible light positioning system using fingerprinting and extreme learning machine (ELM) is proposed to make the system achieve not only high positioning accuracy and elevated anti-interference but also well-behaved real-time ability. In contrast to the positioning system based on K-Nearest Neighbor or Support Vector Machine, the proposed system achieves the highest positioning accuracy and the state-of-the-art positioning speed. Furthermore, the visible light positioning kernel is proposed as a method to reduce the size of the fingerprint database and thus reduce the training time exponentially. Both the simulation and the experiment results show that the proposed system achieves real-time 3-D positioning with high anti-interference. Therefore, this scheme can be considered as one of the effective methods for indoor 3-D positioning.