Title :
An improved CDKF algorithm based on RBF neural network for satellite attitude determination
Author :
Dong Xinyuan ; Wu Jinjie ; Wang Sufeng ; Chen Ting
Author_Institution :
Inst. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China
Abstract :
In this paper, an improved Central divided-Difference Kalman Filter (CDKF) algorithm for the satellite that adopts three-axis magnetometer (TAM) and a fiber optic gyroscope (FOG) as attitude sensors is proposed. First, to overcome the disadvantages of Extended Kalman Filter (EKF), which is widely used as the attitude determination technique of satellites at the present, we present Central divided-Central Kalman Filtering (CDKF) algorithm applicable on the basis of the discretization kinematics equation of satellite, and applicable to attitude determination of this type of satellite. Then, considering the uncertainties and external disturbances on the system, we utilize RBF neural network to adjust the filtering gain online. Finally, a comparative simulation experiment is provided to show that our RBF-CDKF algorithm is able to achieve greater estimation performance than EKF and CDKF.
Keywords :
Kalman filters; aerospace computing; artificial satellites; gyroscopes; magnetometers; radial basis function networks; sensors; EKF; FOG; RBF neural network; RBF-CDKF algorithm; TAM; attitude sensor; central divided-central Kalman filtering; extended Kalman filter; fiber optic gyroscope; filtering gain; radial basis function network; satellite attitude determination; satellite discretization kinematics equation; three-axis magnetometer; Gyroscopes; Magnetic field measurement; Magnetometers; Mathematical model; Neural networks; Position measurement; Satellites; Central divided-Difference Kalman Filter; Extended Kalman Filter; RBF; attitude determination;
Conference_Titel :
Image Analysis and Signal Processing (IASP), 2012 International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-2547-9
DOI :
10.1109/IASP.2012.6425013
