Autonomous optical navigation based on Adaptive SR-UKF for deep space probes

Author

Ma Lichao ; Liu Zaozhen ; Meng Xiuyun

Author_Institution

Sch. of Aerosp. Eng., Beijing Inst. of Technol., Beijing, China

fYear

2010

fDate

29-31 July 2010

Firstpage

321

Lastpage

325

Abstract

A novel adaptive Square-Root Unscented Kalman Filter (Adaptive SR-UKF) was proposed to solve the problem that the priori noise statistics are difficult to obtain accurately in an autonomous optical navigation for deep space probes. The adaptive SR-UKF realizes its initialization by using an approximation of the priori noise statistics, which is corrected at each step by the adaptive SR-UKF that adopts a limited memory algorithm for the estimation of the noise statistics based on noise samples. Effectiveness of the Adaptive SR-UKF is examined by the simulation of an autonomous optical navigation for the cruise phase of a cislunar probe, and results show that the Adaptive SR-UKF is much superior to UKF and SR-UKF when the priori noise statistics are inaccurate.

Keywords

adaptive Kalman filters; estimation theory; inertial navigation; statistical analysis; adaptive SR-UKF; adaptive square root unscented Kalman filter; autonomous optical navigation; deep space probes; limited memory algorithm; noise statistics estimation; Adaptive optics; Navigation; Noise; Nonlinear optics; Optical filters; Optical imaging; Optical noise; Adaptive square-root unscented Kalman filter; Autonomous optical navigation; Deep space probe;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (CCC), 2010 29th Chinese

Conference_Location

Beijing

Print_ISBN

978-1-4244-6263-6

Type

conf

Filename

5573214