DocumentCode :
1795254
Title :
The research of satellite attitude determination algorithm based on federal filter
Author :
Jing Yang ; Xiaoman Zhang
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
fYear :
2014
fDate :
8-10 Aug. 2014
Firstpage :
2122
Lastpage :
2127
Abstract :
In order to obtain satellite attitude with better precision and reliability by using a multi-sensor satellite attitude determination system with uncertain model error, federated filter structure is adopted. The subfilter of federated filter is an improved Nonlinear filter, which is suitable for estimation of nonlinear system with uncertain model error by combining the Predictive filter and second-order divided Difference Filter (NPDF). The proposed method estimate model error and system state estimation by fusion multi-sensor information. The simulation results show that the federated filter with feedback, compared with no feedback, obtained better precision that is similar with that of centralized filter. 5 kinds of information-sharing methods are compared, Frobenius norm of estimated error variance matrix algorithm and variance matrix eigenvalue trace algorithm obtained relatively better estimation precision.
Keywords :
attitude measurement; eigenvalues and eigenfunctions; nonlinear filters; sensor fusion; space vehicles; state estimation; Frobenius norm; NPDF; difference filter; estimation precision; federal filter; fusion multisensor information; information-sharing methods; multisensor satellite attitude determination system; nonlinear filter; predictive filter; satellite attitude determination algorithm; system state estimation; uncertain model error; variance matrix eigenvalue trace algorithm; Filtering algorithms; Information filters; Mathematical model; Satellites; Sun; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4799-4700-3
Type :
conf
DOI :
10.1109/CGNCC.2014.7007503
Filename :
7007503
Link To Document :
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