Ultra Wideband
Ultra-wideband (UWB) technology offers high-accuracy localization capabilities, primarily focusing on overcoming challenges like multipath interference and non-line-of-sight (NLOS) propagation. Current research emphasizes improving UWB's accuracy and robustness in complex, large-scale environments through techniques such as machine learning (e.g., deep learning, Kalman filtering variants), sensor fusion (integrating UWB with LiDAR, IMU, or cameras), and novel signal processing methods for NLOS mitigation and ranging error correction. This work has significant implications for various applications, including robotics, autonomous navigation, human-computer interaction, and healthcare, by enabling precise and reliable positioning in diverse settings.
Papers
Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System
Fuhu Che, Qasim Zeeshan Ahmed, Jaron Fontaine, Ben Van Herbruggen, Adnan Shahid, Eli De Poorter, Pavlos I. Lazaridis
Novel Fine-Tuned Attribute Weighted Na\"ive Bayes NLoS Classifier for UWB Positioning
Fuhu Che, Qasim Zeeshan Ahmed, Fahd Ahmed Khan, Faheem A. Khan