Building Floor Identification Method Based on DAE-LSTM in Cellular Network

Abstract

Quick and accurate floor identification in a multistory building is a challenging task for 3D indoor positioning. The performances of the available methods are low accuracy or even unworkable in the large and complex urban environment due to the noisy received data. Furthermore, the relationship between received data from different reference points is not considered to make the floor identification better. Therefore, in this paper, we focus on improving floor identification accuracy and propose a novel floor identification method. For better describing the property of the signal propagating difference coming from the same base station to different floors, we analyze both the channel characteristics and geographic characteristics and then select five important parameters for floor identification. Based on these parameters, a floor identification method is proposed. We use Denoising Autoencoder (DAE) on these parameters for noise reduction and feature extraction. Then, we use the Long Short-Term Memory (LSTM) on the denoised features for floor identification, which can better explore and utilize data feature relationship. Based on the real cellular network data, the experiment results show that our proposed floor identification method is very accurate for different structural buildings, which outperforms the traditional methods.