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

TL;DR

This paper proposes a novel anomaly detection method using Generalized Gaussian Distribution to improve NLoS signal identification in UWB indoor positioning, enhancing accuracy in challenging scenarios with few NLoS components.

Contribution

It introduces a GGD-based anomaly detection approach specifically designed for NLoS component identification in UWB IPS, addressing limitations of existing machine learning methods.

Findings

Improved NLoS signal classification accuracy.

Enhanced UWB positioning accuracy in NLoS conditions.

Robust detection with small NLoS component data.

Abstract

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.