Kavli Affiliate: Zeeshan Ahmed
| First 5 Authors: Fuhu Che, Qasim Zeeshan Ahmed, Faheem A. Khan, Pavlos I. Lazaridis,
| Summary:
With the rapid development of the Internet of Things (IoT), Indoor
Positioning System (IPS) has attracted significant interest in academic
research. Ultra-Wideband (UWB) is an emerging technology that can be employed
for IPS as it offers centimetre-level accuracy. However, the UWB system still
faces several technical challenges in practice, one of which is
Non-Line-of-Sight (NLoS) signal propagation. Several machine learning
approaches have been applied for the NLoS component identification. However,
when the data contains a very small amount of NLoS components it becomes very
difficult for existing algorithms to classify them. This paper focuses on
employing an anomaly detection approach based on Gaussian Distribution (GD) and
Generalized Gaussian Distribution (GGD) algorithms to detect and identify the
NLoS components. The simulation results indicate that the proposed approach can
provide a robust NLoS component identification which improves the NLoS signal
classification accuracy which results in significant improvement in the UWB
positioning system.
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