Centimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks

Jianyuan Yu, Hussein Metwaly Saad and R. Michael Buehrer

Abstract: This paper aims at solving two major drawbacks of fingerprint-based localization methods: 1) existing fingerprintbased methods mainly rely on the received signal strength indicator (RSSI) as the input feature, which renders centimeterlevel localization impossible; 2) existing deep learning methods that rely on channel state information (CSI) as a feature do not consider the user trajectory and/or the signal-to-noise-ratio (SNR) information. To address these issues, this paper introduces a recurrent neural network (RNN) for centimeter-level indoor localization. The proposed RNN takes into consideration the user trajectory, as well as, the SNR information. We show that when the training data set is small, our proposed network beats the state-of-the-art neural networks. Moreover, for the first time, we present an extensive comparison between neural-network-based and decision-tree-based localization methods. The simulation results show that neural networks have higher estimation accuracy than tree-based methods.
Published in: 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 20 - 23, 2020
Hilton Portland Downtown
Portland, Oregon
Pages: 1317 - 1323
Cite this article: Yu, Jianyuan, Saad, Hussein Metwaly, Buehrer, R. Michael, "Centimeter-Level Indoor Localization using Channel State Information with Recurrent Neural Networks," 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), Portland, Oregon, April 2020, pp. 1317-1323.
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