Work

A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-Based Flight Simulators

Year: 2025

Type: article

Abstract: This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming.

Source: IEEE Transactions on Computers

DOI: 10.1109/tc.2025.3566901

Authors: Onyeka Josephine Nwobodo, Godlove Suila Kuaban, Valery Nkemeni, Kamil Wereszczynski, Krzysztof Adam Cyran

Institutions: Silesian University of Technology, Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, University of Buea

Language: English

Topic: Inertial Sensor and Navigation

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,645

Funder: Silesian University of Technology