Pose estimation of CubeSats via sensor fusion and Error-State Extended Kalman Filter

TL;DR

This paper presents a sensor fusion-based pose estimation method for CubeSats using an error-state extended Kalman filter that integrates angular rates, accelerations, and range measurements, validated through simulations and experiments.

Contribution

It introduces a novel error-state EKF framework for CubeSat pose estimation combining multiple sensors and demonstrates its effectiveness through simulations and experimental tests.

Findings

Effective pose estimation in simulated scenarios.

Successful experimental validation with a 3-DOF mock-up.

Improved accuracy over traditional methods.

Abstract

A pose estimation technique based on error-state extended Kalman that fuses angular rates, accelerations, and relative range measurements is presented in this paper. An unconstrained dynamic model with kinematic coupling for a thrust-capable satellite is considered for the state propagation, and a pragmatic measurement model of the rate gyroscope, accelerometer, and an ultra-wideband radio are leveraged for the measurement update. The error-state extended Kalman filter framework is formulated for pose estimation, and its performance has been analyzed via several simulation scenarios. An application of the pose estimator for proximity operations and scaffolding formation of CubeSat deputies relative to their mother-ship is outlined. Finally, the performance of the error-state extended Kalman filter is demonstrated using experimental analysis consisting of a 3-DOF thrust cable satellite mock-up, rate gyroscope, accelerometer, and ultra-wideband radar modules.