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Robust adaptive unscented Kalman filter for attitude estimation of pico satellites
Author(s) -
Hajiyev Chingiz,
Soken Halil Ersin
Publication year - 2014
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2393
Subject(s) - kalman filter , control theory (sociology) , covariance , extended kalman filter , nonlinear system , noise (video) , computer science , fault (geology) , unscented transform , algorithm , invariant extended kalman filter , mathematics , artificial intelligence , statistics , control (management) , physics , quantum mechanics , seismology , image (mathematics) , geology
SUMMARY Unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for the estimation problems of nonlinear systems even when high nonlinearity is in question. However, in case of system uncertainty or measurement malfunctions, the UKF becomes inaccurate and diverges by time. This study introduces a fault‐tolerant attitude estimation algorithm for pico satellites. The algorithm uses a robust adaptive UKF, which performs correction for the process noise covariance (Q‐adaptation) or measurement noise covariance (R‐adaptation) depending on the type of the fault. By the use of a newly proposed adaptation scheme for the conventional UKF algorithm, the fault is detected and isolated, and the essential adaptation procedure is followed in accordance with the fault type. The proposed algorithm is tested as a part of the attitude estimation algorithm of a pico satellite. Copyright © 2013 John Wiley & Sons, Ltd.