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Multiple Robust High-degree Cubature Kalman Filter for Relative Position and Attitude Estimation of Satellite Formation

Published online by Cambridge University Press:  15 April 2019

Ning Li ,
Wentao Ma ,
Weishi Man ,
Liu Cao  and
Hui Zhang
Ning Li*
Affiliation:
(School of Automation and Information Engineering, Xi'an University of Technology, 710048, Xi'an, China) (State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, 710048, Xi'an, China)
Wentao Ma
Affiliation:
(School of Automation and Information Engineering, Xi'an University of Technology, 710048, Xi'an, China)
Weishi Man
Affiliation:
(School of Automation and Information Engineering, Xi'an University of Technology, 710048, Xi'an, China)
Liu Cao
Affiliation:
(School of Automation and Information Engineering, Xi'an University of Technology, 710048, Xi'an, China)
Hui Zhang
Affiliation:
(School of Automation and Information Engineering, Xi'an University of Technology, 710048, Xi'an, China)
*
(E-mail: lining19830809@163.com)
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Abstract

The High-degree Cubature Kalman Filter (HCKF) is proposed as a novel methodology based on the arbitrary degree spherical rule, which can achieve better performance than the traditional Kalman filter. However, it also has a large calculation burden when used in a high-dimension and high-degree of accuracy estimation system. The number of sampling points of an HCKF increases polynomially with increasing state-space dimensions, which further increases the calculation burden. The reduction of the number of the state-space dimensions is the main contribution of this study. A strategy for HCKF based on the partitioning of the state-space and orthogonal principle is introduced, referred to as the Multiple Robust HCKF (MRHCKF). It is shown that this technique can effectively reduce the calculation burden for the high-dimension system with robust performance. Numerical simulations are performed for the example of high-dimension relative position and attitude estimation to show that the proposed method can obtain nearly the same performance as the HCKF, while drastically reducing computational complexity.

Keywords

Cubature Kalman filterMultiple filterSatellite formationRelative position and attitude
Type
Review Article
Information
The Journal of Navigation , Volume 72 , Issue 5 , September 2019 , pp. 1254 - 1274
Copyright
Copyright © The Royal Institute of Navigation 2019 

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