Indoor visible light positioning based on fusion of subregion BES-ELM and WDME weighted dual-mode

Aiming at the problems of low indoor positioning accuracy and large boundary area positioning error, an indoor visible light positioning method based on the bald eagle search algorithm-extreme learning machine (BES-ELM) neural network and weighted dual-mode edge (WDME) positioning model is proposed. In this method, a visible light system structure with a single LED and five photodetectors is proposed, and the room is divided by fuzzy c-means clustering algorithm. BES is used to optimize the ELM neural network, and the BES-ELM positioning model is established in different regions. Aiming at the boundary area, a weighted dual-mode edge (WDME) location model is constructed to achieve accurate edge location. Based on the indoor environment simulation of 3.2 m× 3.2 m× 3 m, the results show that the BES-ELM algorithm is used to locate the center area, and the average positioning error is 0.011 7 m, the minimum positioning error is 0.001 9 m. Using WDME positioning model to locate the edge area, the average positioning error is 0.013 3 m, which improves the positioning accuracy by 84%, 27%, and 26% compared with ELM, Elman and BES-ELM models, respectively. Therefore, the proposed visible light positioning method reduces the overall area positioning error, especially the edge area positioning accuracy is improved.