K-means+SSA-Elman network visible light indoor location awareness algorithm

Due to the complex indoor environment, the visible light location awareness based on Elman neural network has the problems of slow convergence speed and low positioning accuracy. An optimized Elman neural network based on sparrow search algorithm (SSA) was proposed, and a visible light indoor location awareness algorithm was fused with K-means clustering. The database was established for the collected data, the topological structure and connection weight threshold of the Elman were optimized by using SSA, and the training model was designed, so as to solve the problem that the indoor location awareness algorithm based on Elman neural network was easy to fall into the local optimization and improve the convergence speed and robustness. The K-means was used to optimize the classification of database, and the processed data was substituted into the model training to obtain the preliminary prediction results. The preliminary prediction results were substituted into the subclass for secondary training to obtain the final coordinates of predicted position, which further improved the positioning accuracy. The experiment based on three-dimensional space of 0.8 m \times 0.8 m \times 0.8 m was carried out, and the results show that the average positioning error of the proposed algorithm is 3.22 cm, and the probability of positioning error less than 6 cm is 90%, which improves the positioning accuracy by 7.5% compared with the SSA-Elman algorithm and by 16% compared with the Elman network algorithm.