A Floor Distinction Method Based on Recurrent Neural Network in Cellular Network

Indoor localization is nowadays becoming a hot topic and research trend for future large-scale location-aware services, particularly in high-rise buildings with complex structures. However, the indoor positioning methods existing are just with high interests of two-dimensional planar information, and the crucial height information for accurate position result is awfully neglected. Furthermore, without considering the shadow effect caused by indoor constant changing impact on the terminal to be located, positioning methods cannot achieve a desirable localization accuracy for building environment. In this paper, we proposed a fast and reliable method using deep neural network for floor-level distinction and position estimation based on ubiquitous radio waves in mobile communication system. The framework composed of autoencoder to extract the effective feature vectors and recurrent neural network classifier to solve the misclassification caused by timing-discontinuity of received signal. It is shown that the accuracy of floor distinction is over 90.2% in different structural construction environments, which can provide comparable to current top-performing floor localization methods.