Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Attitude Determination System for a CubeSat Experiencing Eclipse

Version 1 : Received: 15 May 2023 / Approved: 16 May 2023 / Online: 16 May 2023 (09:09:13 CEST)

A peer-reviewed article of this Preprint also exists.

Mmopelwa, K.; Ramodimo, T.T.; Matsebe, O.; Basutli, B. Attitude Determination System for a Cubesat Experiencing Eclipse. Sensors 2023, 23, 8549. Mmopelwa, K.; Ramodimo, T.T.; Matsebe, O.; Basutli, B. Attitude Determination System for a Cubesat Experiencing Eclipse. Sensors 2023, 23, 8549.

Abstract

In the context of Kalman filters, the predicted error covariance matrix $\mathbf{P}_{k+1}$ and measurement noise covariance matrix $\mathbf{R}$ are used to represent the uncertainty of state variables and measurement noise, respectively. However, in real-world situations, these matrices may vary with time due to measurement faults. To address this issue in CubeSat attitude estimation, an adaptive extended Kalman filter has been proposed that can dynamically estimate the predicted error covariance matrix and measurement noise covariance matrix using an expectation-maximization approach. Simulation experiments have shown that this algorithm outperforms existing methods in terms of attitude estimation accuracy, particularly in sunlit and shadowed phases of the orbit, with the same filtering parameters and initial conditions.

Keywords

Attitude estimation; expectation maximization; CubeSat; extended Kalman filtering; attitude kinematics

Subject

Engineering, Control and Systems Engineering

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