Magnetometer-based attitude determination algorithm has been studied for decades owing to its advantages. This article aims to develop and fine-tune the magnetometer-based attitude determination method by introducing two new observation models for the extended Kalman filter (EKF). The optimization techniques were purposed to increase accuracy and improve convergence. The Monte Carlo simulation, including disturbances and a moment of inertia estimation error, was held to evaluate the performance of each EKF by the innovation vector and convergence. The significant effect of singularity on the reliability of the estimator was discovered and resolved by optimization. The study concluded that the attitude representation observation EKF with optimizations reduced the convergence time and provided reliability.
Magnetometer-Based Attitude Determination Extended Kalman Filter and Optimization Techniques
IEEE Transactions on Aerospace and Electronic Systems ; 59 , 6 ; 7993-8004
01.12.2023
3846446 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Magnetometer-Only Attitude Determination Using Two-Step Extended Kalman Filter (AAS 11-572)
British Library Conference Proceedings | 2012
|Magnetometer Based Attitude Determination System Using a Reduced Order Kalman Filter
British Library Conference Proceedings | 1996
|Extended Kalman Filter-based Attitude Estimation using Magnetometer- and Sun Sensor-Aided MEMS Gyros
Springer Verlag | 2021
|