What question did this study set out to answer?

The aim is to develop a robust method for estimating parameters of an unknown spacecraft using advanced filtering techniques.

May 21, 2026Open Access

Robust Pose and Inertial Parameter Estimation of An Unknown aircraft Based on Variational BAYESIAN Dual Vector Quaternion Extended Kalman Filter

Key Points

The aim is to develop a robust method for estimating parameters of an unknown spacecraft using advanced filtering techniques.
The study utilizes a variational Bayesian Dual Vector Quaternion Extended Kalman Filter (VB-DVQEKF) for estimation.
Kinematics and dynamics are modeled using dual vector quaternions to account for motion interactions.
Mathematical simulations validate the proposed method against traditional techniques like DVQ-EKF and SH-DVQEKF.
VB-DVQEKF shows the smallest steady-state error compared to conventional methods with a significant reduction in root-mean-square error (RMSE).
Demonstrated stability under various measurement anomalies including white-burst and outlier-type errors.
Achieved faster convergence time and better final estimation error than alternative filtering techniques.

Abstract

Accurately determining the parameters of an unmodeled spacecraft is crucial. Filtering methods that are resilient to uncertainty, employing dual quaternion frameworks to ascertain orientation and position, introduce a design for an extended Kalman filter based on variational Bayesian inference and dual vector quaternions (VB-DVQEKF) to carry out parameter estimation for a non-cooperative spacecraft. The system kinematics and dynamics are modeled using dual vector quaternions, rendering the representation manifestly concise. The method achieves thoroughness by accounting for the coupled interactions between translational and rotational motions. Furthermore, to address uncertainties in the measurements, a variational Bayesian approach is employed for the dependable simultaneous estimation of state parameters and measurement noise covariance. Mathematical simulations are used to verify the proposed VB-DVQEKF, and its robust capabilities are demonstrated through comparisons with several conventional parameter estimation techniques, including the conventional DVQ-EKF and the Sage–Husa adaptive DVQ-EKF (SH-DVQEKF). Quantitative results based on root-mean-square error (RMSE), convergence time, and final estimation error confirm that the proposed VB-DVQEKF achieves the smallest steady-state error among the compared methods and remains stable under white-burst, gradient (drift), and outlier-type measurement anomalies.

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