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CLC number: V448.22

On-line Access: 2010-06-02

Received: 2009-11-14

Revision Accepted: 2010-03-08

Crosschecked: 2010-04-28

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.6 P.455-464

http://doi.org/10.1631/jzus.A0900725


Magnetometer-only linear attitude estimation for bias momentum pico-satellite


Author(s):  Ke Han, Hao Wang, Zhong-he Jin

Affiliation(s):  Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   roger@zju.edu.cn

Key Words:  Pico-satellite, Attitude estimation, Bias momentum, Magnetometer, Kalman filter (KF)


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Ke Han, Hao Wang, Zhong-he Jin. Magnetometer-only linear attitude estimation for bias momentum pico-satellite[J]. Journal of Zhejiang University Science A, 2010, 11(6): 455-464.

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T1 - Magnetometer-only linear attitude estimation for bias momentum pico-satellite
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DOI - 10.1631/jzus.A0900725


Abstract: 
Satellite attitude information is essential for pico-satellite applications requiring light-weight, low-power, and fast-computation characteristics. The objective of this study is to provide a magnetometer-only attitude estimation method for a low-altitude Earth orbit, bias momentum pico-satellite. Based on two assumptions, the spacecraft spherical symmetry and damping of body rates, a linear kinematics model of a bias momentum satellite’s pitch axis is derived, and the linear estimation algorithm is developed. The algorithm combines the linear kalman filter (KF) with the classic three-axis attitude determination method (TRIAD). KF is used to estimate satellite’s pitch axis orientation, while TRIAD is used to obtain information concerning the satellite’s three-axis attitude. Simulation tests confirmed that the algorithm is suited to the time-varying model errors resulting from both assumptions. The estimate result keeps tracking satellite attitude motion during all damping, stable, and free rotating control stages. Compared with nonlinear algorithms, such as extended Kalman filer (EKF) and square root unscented Kalman filer (SRUKF), the algorithm presented here has an almost equal performance in terms of convergence time and estimation accuracy, while the consumption of computing resources is much lower.

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