Wi-Fi Fingerprint-Based Indoor Mobile User Localization Using Deep Learning. (8th January 2021)

• Record Type: Journal Article
• Authors: Bai, Junhang; Sun, Yongliang; Meng, Weixiao; Li, Cheng
• Other Names: Liu Xin Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-01-08
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/6660990
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.