Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System

Fuhu Che, Qasim Ahmed, Jaron Fontaine, Ben Van Herbruggen , Adnan Shahid, Eli De Poorter, Pavlos Lazaridis

Research output: Contribution to journalArticlepeer-review

Abstract

Non-Line-of-Sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in Ultra-Wideband (UWB) Indoor Positioning System (IPS). Numerous supervised Machine Learning (ML) approaches have been applied for NLoS identification to improve the accuracy of the IPS. 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. The inaccurate localization of the target node caused by these small number of NLoS signals can still be problematic. To solve this issue, we propose features-based Gaussian Distribution (GD) and Generalized Gaussian Distribution (GGD) NLoS detection algorithm for imbalanced LoS and NLoS signals. By employing our detection algorithm for the imbalanced dataset, the NLoS classification accuracy can achieve 96.7% for GD and 98.0% for GGD. We also compared the proposed algorithm with the existing cutting-edge such as Support-Vector-Machine (SVM), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN), which can achieve an accuracy of 92.6%,92.8%,93.2% and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset and also achieve a high classification accuracy for different ratios of LoS and NLoS signals which proves the robustness and effectiveness of the algorithm.
Original languageEnglish
JournalIEEE Sensors Journal
Publication statusAccepted/In press - 29 Jul 2022

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