Spacecraft relative navigation based on multiple model adaptive estimator

Author

Xiong Kai ; Wei Chunling ; Liu Liangdong

Author_Institution

Beijing Inst. of Control Eng., Beijing, China

fYear

2014

fDate

28-30 July 2014

Firstpage

798

Lastpage

803

Abstract

This paper studies the multiple model adaptive estimator (MMAE) for nonlinear systems with unknown disturbances. Multiple models are constructed with a set of process noise covariance matrices, such that the algorithm that consists of multiple parallel filters can adapt to different levels of unknown disturbances. The filtering stability of the MMAE is analyzed. Sufficient conditions to ensure the boundedness of the algorithm is provided. A performance comparison among an extended Kalman filter (EKF), a nonlinear robust filter (NRF) and the MMAE is carried out for spacecraft relative navigation, where the position of a space target is estimated by using double line-of-sight (LOS) measurements. Simulation studies illustrate that the MMAE performs better than the EKF and the NRF.

Keywords

Kalman filters; adaptive estimation; aircraft navigation; covariance matrices; filtering theory; nonlinear filters; nonlinear systems; stability; EKF; LOS measurements; MMAE; NRF; double line-of-sight measurement; extended Kalman filter; filtering stability; multiple model adaptive estimator; multiple parallel filters; nonlinear robust filter; nonlinear systems; performance comparison; process noise covariance matrices; space target; spacecraft relative navigation; sufficient conditions; unknown disturbance; Adaptation models; Electronic mail; Filtering algorithms; Kalman filters; Navigation; Robustness; Space vehicles; multiple model adaptive estimator; robust Kalman filter; spacecraft relative navigation;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2014 33rd Chinese

Conference_Location

Nanjing

Type

conf

DOI

10.1109/ChiCC.2014.6896729

Filename

6896729