Fusion of Time and Angle Measurements for Digital-Twin-Aided Probabilistic 3D Positioning

TL;DR

This paper extends 2D indoor positioning methods to 3D environments by integrating angle and time measurements, proposing efficient data fusion algorithms and demonstrating their effectiveness through simulations.

Contribution

It generalizes existing 2D positioning techniques to 3D and introduces novel algorithms for fusing AoA and propagation-time data.

Findings

Enhanced 3D positioning accuracy demonstrated

Effective data fusion algorithms developed

Simulation results show improved performance

Abstract

Previous studies explained how the 2D positioning problem in indoor non line-of-sight environments can be addressed using ray tracing with noisy angle of arrival (AoA) measurements. In this work, we generalize these results on two aspects. First, we outline how to adapt the proposed methods to address the 3D positioning problem. Second, we introduce efficient algorithms for data fusion, where propagation-time or relative propagation-time measurements (obtained via e.g., the time difference of arrival) are used in addition to AoA measurements. Simulation results are provided to illustrate the advantages of the approach.