Comparison of small satellite attitude determination methods

Author(s):  
Sonia Marques ◽  
Roberts Clements ◽  
Pedro Lima
Author(s):  
Liang He ◽  
Wenjie Ma ◽  
Pengyu Guo ◽  
Tao Sheng

This study surveys the developments in satellite attitude determination and control system, especially for microsats. This survey is not intended to be complete but is limited to the most significant developments of sensors, actuators, and algorithms in the last two decades. First, attitude determination methods including algorithms and sensors together with actuator-based control methods are introduced. Furthermore, current problems in alignment error, flexible satellites, and low redundancy of microsats attitude determination and control system are discussed. Moreover, developments of some deep-neural-networks-based methods, which have great potential in solving current problems, are summarized.


2021 ◽  
Author(s):  
Yaroslav Mashtakov ◽  
Uliana Monakhova ◽  
Danil Ivanov ◽  
Stepan Tkachev ◽  
Alexey Shestoperov ◽  
...  

2016 ◽  
Vol 129 ◽  
pp. 52-58 ◽  
Author(s):  
Osama Khurshid ◽  
Jorma Selkäinaho ◽  
Halil Ersin Soken ◽  
Esa Kallio ◽  
Arto Visala

2005 ◽  
Vol 5 (10) ◽  
pp. 1739-1743 ◽  
Author(s):  
S. Chouraqui ◽  
M. Benyettou . ◽  
A. Si Mohammed .

1987 ◽  
Author(s):  
Robert L. Russell ◽  
Andrew J. D' Arcy

Author(s):  
Baojian Yang ◽  
Lu Cao ◽  
Dechao Ran ◽  
Bing Xiao

Due to unavoidable factors, heavy-tailed noise appears in satellite attitude estimation. Traditional Kalman filter is prone to performance degradation and even filtering divergence when facing non-Gaussian noise. The existing robust algorithms have limited accuracy. To improve the attitude determination accuracy under non-Gaussian noise, we use the centered error entropy (CEE) criterion to derive a new filter named centered error entropy Kalman filter (CEEKF). CEEKF is formed by maximizing the CEE cost function. In the CEEKF algorithm, the prior state values are transmitted the same as the classical Kalman filter, and the posterior states are calculated by the fixed-point iteration method. The CEE EKF (CEE-EKF) algorithm is also derived to improve filtering accuracy in the case of the nonlinear system. We also give the convergence conditions of the iteration algorithm and the computational complexity analysis of CEEKF. The results of the two simulation examples validate the robustness of the algorithm we presented.


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