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-extreme learning machine(BES-ELM)neural network and weighted dual-mode edge(WDME)positioning model was proposed.In this method,a visible light system structure with a single LED and five photodetectors was proposed,and the room was divided by fuzzy c-means clustering algorithm.The BES was used to optimize the ELM neural network,and the BES-ELM positioning model was established in different regions.Aiming at the boundary area,a weighted dual-mode edge(WDME)positioning model was 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 using the BES-ELM algorithm to locate the center area,the average positioning error is 0.011 7 m,and the minimum positioning error is 0.001 9 m.Using the WDME positioning model to locate the edge area,the average positioning error is 0.013 3 m,which is 84％,27％,and 26％higher than that of ELM,Elman and BES-ELM models,respectively.Therefore,the proposed visible light positioning method reduces the overall area positioning errors,especially improving the positioning accuracy of edge area.
万方数据
维普
