When conventional EKF (extended Kalman filtering) algorithm was used in LEO (low earth orbit satellite) orbit determination by space-borne GPS (global positioning system). It is difficult to know the initial value of system noise variance. Therefore, the novel filter algorithm of orbit determination was proposed. In the new algorithm, the methods of linearization at the prior filtering estimate value was adopted during the linearization process of the nonlinear equations, and the disturbing equations were obtained, and then the disturbing equations were introduced in conventional EKF. Apply the EKF and the modified algorithm in LEO orbit determination based on space-borne GPS respectively, the results show the modified algorithm can restrain filter divergence of conventional EKF caused by the large deflection of system noise variance in a certain extent, and has better robustness to the selecting of initial value of system noise variance.
Wada M, Yoon K S , Hashimoto H. Nonlinear filter road vehicle model development // 4th IEEE International Conference on Intelligent Transportation Systems. Oakland: , 2001:734-739
Bucy R S,Renne K D. Digital synthesis of nonlinear filter[J].Automatica.1971, 7(3):287-289
���,������.�����㶯����������������ĵ��������о�[J].�й��ռ��ѧ����,2003,10(5):57-63 Zhang Yu, Fang Jiancheng. Simulation and research on the satellite autonomous celestial navigation based on the perturbative orbit [J]. Chinese Space Science and Technology, 2003, 10(5):57-63 (in Chinese)
��Сƽ.��������������Ӧ��[M].��ɳ:�����Ƽ���ѧ������,2002 Hu Xiaoping. Autonomous navigation and application [M]. Changsha: National University of Defense Technology Press, 2002 (in Chinese)
����Ԫ,�ź���,���廪.�������˲�����ϵ���ԭ��[M]. ����:������ҵ��ѧ������,1998 Qin Yongyuan, Zhang Hongyue, Wang Shuhua.Kalman filter and integrated navigation principle [M]. Shanxi: Northwestern Polytechnical University Press, 1998 (in Chinese)
�����,�����.������ϵͳ�п������˲���һ�������Ի�����[J].�人��ѧѧ��·��Ϣ��ѧ��,2004,29(4):346-348 Sun Hongxing, Li Deren. A new linearization method for the Kalman filter in nonlinear system [J]. Geomatics and Information Science of Wuhan University, 2004,29(4):346-348 (in Chinese)