A higher-order robust correlation Kalman filter for satellite attitude estimation

Xiaoqian Chen; Lu Cao; Pengyu Guo; Bing Xiao

doi:10.1016/j.isatra.2019.12.009

A higher-order robust correlation Kalman filter for satellite attitude estimation

ISA Trans. 2022 May:124:326-337. doi: 10.1016/j.isatra.2019.12.009. Epub 2019 Dec 30.

Authors

Xiaoqian Chen¹, Lu Cao², Pengyu Guo³, Bing Xiao⁴

Affiliations

¹ Research Center of Unmanned Systems Technology, National Innovation Institute of Defense Technology, Beijing 100071, China. Electronic address: chenxiaoqian@nudt.edu.cn.
² Research Center of Unmanned Systems Technology, National Innovation Institute of Defense Technology, Beijing 100071, China. Electronic address: caolu_space2015@163.com.
³ Research Center of Unmanned Systems Technology, National Innovation Institute of Defense Technology, Beijing 100071, China. Electronic address: pengyu.guo@nudt.edu.cn.
⁴ School of Automation, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: xiaobing@nwpu.edu.cn.

PMID: 31948682
DOI: 10.1016/j.isatra.2019.12.009

Abstract

A higher-order robust correlation Kalman filtering approach is presented to achieve attitude estimation for satellites with unknown modeling errors. A robust correlation Kalman filter (RCKF) is preliminarily derived by using the sequence orthogonal principle. To improve its performance further, a higher-order sigma version of the RCKF is designed by incorporating a novel sigma point generation algorithm. This modified filter can capture the third and the fourth central moment's information of the system posteriori probability density function. It is proved that the proposed filter achieves better estimation accuracy and robustness. The effectiveness of the developed filter is demonstrated by simulation results with it applied to the satellite attitude estimation problem.

Keywords: Attitude estimation; High-order filter; Modeling error; Satellite; Sigma point.