Fingerprinting-based Indoor Localization in a 3x3 Meter Grid Using OFDM
Signals at Sub-6 GHz
Abstract
Accurately determining the indoor location of mobile devices has
garnered great interest due to its significant challenge in locating
sources due to non-line-of-sight 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 subcarrier amplitude and phase differences from CSI data to
create fingerprints, which are then clustered to identify the number of
groups of data and split into two sub-databases using a threshold. The
ML algorithms and network architecture are used to train both
sub-databases of fingerprints. Experiments conducted in a standard
indoor environment demonstrate the effectiveness of the proposed
method.
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