Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79372
Title: Gain-scheduled extended Kalman filter for nanosatellite attitude determination system
Authors: Pham, Minh Duc
Low, Kay Soon
Goh, Shu Ting
Chen, Shoushun
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems
Issue Date: 2015
Source: Pham, M. D., Low, K. S., Goh, S. T., & Chen, S. (2015). Gain-scheduled extended Kalman filter for nanosatellite attitude determination system. IEEE transactions on aerospace and electronic systems, 51(2), 1017-1028.
Series/Report no.: IEEE transactions on aerospace and electronic systems
Abstract: Extended Kalman filter (EKF) has been widely used for attitude determination in various satellite missions. However, it requires an extensive computational power which is not suitable for nano-satellite application. This paper proposes a gain-scheduled EKF (GSEKF) to reduce the computational requirement in nano-satellite attitude determination process. The proposed GSEKF eliminates the online recursive Kalman gain computation by analytically determines the Kalman gain based on the sensor parameters, such as the gyroscope noise variance, the quaternion variance, the observation matrix and the satellite rotational speed. Two GSEKF Kalman gains for two satellite operating modes are presented: the sun pointing and nadir pointing modes. The simulation and experimental results show that the proposed method has comparable attitude estimation accuracy to the conventional EKF. In addition, the proposed GSEKF reduces 86.29% and 89.45% of the computation load compared to the multiplicative EKF and Murrell’s version.
URI: https://hdl.handle.net/10356/79372
http://hdl.handle.net/10220/38331
ISSN: 0018-9251
DOI: 10.1109/TAES.2014.130204
Schools: School of Electrical and Electronic Engineering 
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TAES.2014.130204].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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