Small satellite attitude determination based on GPS/IMU data fusion

2014 ◽  
Author(s):  
Andrey Golovan ◽  
Ali Cepe
Keyword(s):  
Data Fusion ◽  
Attitude Determination ◽  
Small Satellite ◽  
Satellite Attitude

Study of the accuracy provided by small satellite attitude determination & control system

2021 ◽  
Author(s):  
Yaroslav Mashtakov ◽  
Uliana Monakhova ◽  
Danil Ivanov ◽  
Stepan Tkachev ◽  
Alexey Shestoperov ◽  
...  
Keyword(s):  
Control System ◽  
Attitude Determination ◽  
Small Satellite ◽  
Satellite Attitude

Small satellite attitude determination during plasma brake deorbiting experiment

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

Sensor Vectors Modeling for Small Satellite Attitude Determination

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

Video-Based Satellite Attitude Determination

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

Centered error entropy Kalman filter with application to satellite attitude determination

2021 ◽  
pp. 014233122110198
Author(s):  
Baojian Yang ◽  
Lu Cao ◽  
Dechao Ran ◽  
Bing Xiao
Keyword(s):  
Kalman Filter ◽  
Gaussian Noise ◽  
Attitude Determination ◽  
Complexity Analysis ◽  
Iteration Algorithm ◽  
Robust Algorithms ◽  
Convergence Conditions ◽  
Satellite Attitude ◽  
Heavy Tailed ◽  
Non Gaussian

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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