Fingerprinting-Based Indoor Localization in a 3 × 3 Meter Grid Using OFDM Signals at Sub-6 GHz

Abstract

Accurately determining the indoor location of mobile devices has garnered significant interest due to the complex challenges posed by non-line-of-sight (NLOS) propagation and multipath effects. To address this challenge, this paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve accuracy. The proposed method extracts the amplitude and phase differences of the subcarriers from the CSI data to create fingerprints. ML algorithms and network architecture are utilised to train the CSI data from two antennas, in the form of phase and amplitude. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.