3D Positioning using a New Diffraction Path Model

TL;DR

This paper introduces a novel diffraction-based path model using GTD to enhance 3D indoor positioning accuracy in emergency scenarios with UAV networks, addressing NLOS and infrastructure dependence issues.

Contribution

It presents a new NLOS path length model based on diffraction and two techniques leveraging this model to improve indoor positioning accuracy during emergencies.

Findings

Improved 3D positioning accuracy demonstrated

Effective mitigation of NLOS measurement bias

Enhanced robustness of UAV-based indoor positioning systems

Abstract

Enhancing 3D and Z-axis positioning accuracy is crucial for effective rescue in indoor emergencies, ensuring safety for emergency responders and at-risk individuals. Additionally, reducing the dependence of a positioning system on fixed infrastructure is crucial, given its vulnerability to power failures and damage during emergencies. Further challenges from a signal propagation perspective include poor indoor signal coverage, multipath effects and the problem of Non-Line-OfSight (NLOS) measurement bias. In this study, we utilize the mobility provided by a rapidly deployable Uncrewed Aerial Vehicle (UAV) based wireless network to address these challenges. We recognize diffraction from window edges as a crucial signal propagation mechanism and employ the Geometrical Theory of Diffraction (GTD) to introduce a novel NLOS path length model. Using this path length model, we propose two different techniques to improve the indoor positioning performance for emergency scenarios.