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With the rapid development of the Internet of Things (IoT), IndoorPositioning System (IPS) has attracted significant interest in academicresearch. Ultra-Wideband (UWB) is an emerging technology that can be employedfor IPS as it offers centimetre-level accuracy. However, the UWB system stillfaces several technical challenges in practice, one of which isNon-Line-of-Sight (NLoS) signal propagation. Several machine learningapproaches have been applied for the NLoS component identification. However,when the data contains a very small amount of NLoS components it becomes verydifficult for existing algorithms to classify them. This paper focuses onemploying an anomaly detection approach based on Gaussian Distribution (GD) andGeneralized Gaussian Distribution (GGD) algorithms to detect and identify theNLoS components. The simulation results indicate that the proposed approach canprovide a robust NLoS component identification which improves the NLoS signalclassification accuracy which results in significant improvement in the UWBpositioning system.

Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System