DocumentCode :
1783173
Title :
X-ray pulsar-based autonomous navigation based on asynchronous observation ST-REKF
Author :
Sun Jian ; Guo Xiliang ; Guo Pengbin ; Li Bin
Author_Institution :
State Key Lab. for Strength & Vibration, Xi´an Jiaotong Univ., Xi´an, China
fYear :
2014
fDate :
28-29 Sept. 2014
Firstpage :
1
Lastpage :
6
Abstract :
X-ray pulsar, as a natural star, has a high precision rotation period and strong antijamming capability. Its high stability and wide range of application is very suitable for spacecraft autonomous navigation. This paper presents an approach for the X-ray pulsar autonomous navigation based on asynchronous observation strong tracking robust kalman filter. The asynchronous observation model can more appropriately and more accurately describe the actual pulsar observation circumstances than the traditional synchronous observation model does. Navigation methods based on this observation model can achieve better real-time performance as well as higher positing accuracy. The approach presented in this paper using strong tracking robust kalman filter (ST-REKF) based on the asynchronous observation model for the pulsar positioning system can effectively improve the robustness and tracking ability to the actual state of the nonlinear system with uncertainties even when jumps occur after the system becomes smooth and steady, which can not be performed well by extended kalman filter (EKF).
Keywords :
Kalman filters; jamming; nonlinear filters; space vehicle navigation; target tracking; EKF; ST-REKF; X-ray pulsar autonomous navigation; asynchronous observation model; natural star; nonlinear system; precision rotation period; spacecraft autonomous navigation; strong antijamming capability; strong tracking robust extended Kalman filter; Fading; Kalman filters; Photonics; Radio navigation; Robustness; Satellite navigation systems; Autonomous navigation; asynchronous observation; robust; strong tracking; x-ray pulsar;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6731-5
Type :
conf
DOI :
10.1109/MFI.2014.6997728
Filename :
6997728
Link To Document :
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