no code implementations • 14 Apr 2023 • Fuhu Che, Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli de Poorter, Pavlos I. Lazaridis
However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of Line-of-Sight (LoS) signals.
no code implementations • 14 Apr 2023 • Fuhu Che, Qasim Zeeshan Ahmed, Fahd Ahmed Khan, Faheem A. Khan
In this paper, we propose a novel Fine-Tuned attribute Weighted Na\"ive Bayes (FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System (IPS).
no code implementations • 9 Aug 2021 • Fuhu Che, Qasim Zeeshan Ahmed, Faheem A. Khan, Pavlos I. Lazaridis
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.