Open AccessCelestial navigation in deep space exploration using spherical simplex unscented particle filter
Author(s) -
Zhao Fangfang,
Ge Shuzhi Sam,
Zhang Jie,
He Wei
Publication year - 2018
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2017.0184
Subject(s) - kalman filter , unscented transform , computer science , simplex , particle filter , extended kalman filter , filter (signal processing) , celestial navigation , simplex algorithm , algorithm , computer vision , artificial intelligence , control theory (sociology) , ensemble kalman filter , mathematics , physics , linear programming , geometry , control (management) , astronomy
Deep space exploration has significant meaning both in science and economy; however, it is very hard to obtain the relevant information due to its complexity. In this study, the autonomous celestial navigation method is utilised. To achieve high accuracy of the celestial navigation in a deep space environment, the improved filtering algorithm–spherical simplex unscented particle filter (SSUPF) is implemented, which adopts the spherical simplex unscented Kalman filter (SSUKF) algorithm to generate the important sampling density of particle filter (PF). According to simulation results, the authors derive that the SSUPF method can greatly increase the performance of the navigation system compared with unscented Kalman filter (UKF), SSUKF and unscented PF (UPF), and the computational burden of SSUPF is reduced by 24% in comparison with UPF.