Research on indoor positioning algorithm based on BP neural network

Abstract

The neural network positioning algorithm is an algorithm that uses the principle of electromagnetic induction to activate a short-range wireless tag wirelessly to achieve information reading. It has the advantages of small size, low cost, and reusability. This paper compares several neural network structures of DNN, CNN, and RNN, and selects BP neural network to optimize its training through theory and practice, combining the data to compare their average error and average time used in indoor positioning practice, the average error of the optimized BP neural network in this article is smaller, and the positioning time is shorter, and combined with the practical data to obtain the efficient use of the division value n, which meets the high-precision requirements of indoor positioning and is more convenient for practical applications.