Unscented Kalman filter with application to orbit determination based on space-borne GPS

doi:10.3778/j.issn.1002-8331.2008.33.065

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (33): 215-217.DOI: 10.3778/j.issn.1002-8331.2008.33.065

• 工程与应用 • Previous Articles Next Articles

Unscented Kalman filter with application to orbit determination based on space-borne GPS

WU Zhi-hua^1,2,SHEN Gong-xun¹,DING Yang-bin¹

1.School of Aeronautics，Beijing University of Aeronautics and Astronautics，Beijing 100083，China
2.Institute of Electronic and Computer Science and Technology，North University of China，Taiyuan 030051，China

Received:2007-12-10 Revised:2008-03-04 Online:2008-11-21 Published:2008-11-21
Contact: WU Zhi-hua

UKF算法在星载GPS低轨卫星定轨中的应用

吴志华^1,2,申功勋¹,丁杨斌¹

1.北京航空航天大学宇航学院，北京 100083
2.中北大学电子与计算机科学技术学院，太原 030051

通讯作者: 吴志华

Abstract

Abstract: Linearization error is introduced in the linearization process of extended Kalman，it can lead divergence of the EKF.To overcome this limitation，UKF algorithm is used directly to nonlinear system in this paper.Linearization is not needed to nonlinear system，so linearization error is voided.This algorithm is used in orbit determination based on space-borne GPS，emulation model is established.Under the same initial condition，the performance of EKF and UKF are compared by simulation，it is shown the performance of UKF algorithm is more precise and has higher accuracy in orbit determination under the certain observation noise level.

Key words: nonlinear system, Unscented Kalman Filter, space-borne GPS, orbit determination

摘要： 对于扩展卡尔曼滤波在非线性系统中由于线性化过程引入了线性化误差，从而导致滤波器性能下降甚至造成滤波发散的情况，利用Unscented卡尔曼滤波器对非线性系统进行直接滤波，该方法无需对非线性系统进行线性化，避免了线性化误差。并将该算法用于星载GPS低轨卫星定轨中，建立了仿真模型，在初始条件相同的情况下，与EKF算法仿真结果相比较，结果表明在一定观测噪声水平下，UKF定轨结果更准确，定轨精度更高。

关键词: 非线性系统, Unscenteel卡尔曼滤波器（UKF）, 星载GPS, 定轨

WU Zhi-hua^1,2,SHEN Gong-xun¹,DING Yang-bin¹. Unscented Kalman filter with application to orbit determination based on space-borne GPS[J]. Computer Engineering and Applications, 2008, 44(33): 215-217.

吴志华^1,2,申功勋¹,丁杨斌¹. UKF算法在星载GPS低轨卫星定轨中的应用[J]. 计算机工程与应用, 2008, 44(33): 215-217.

[1]	LIANG Jiaqi, BU Xuhui, LIU Jian. Iterative learning consensus tracking control for a class of multi-agent systems with data dropouts [J]. Computer Engineering and Applications, 2018, 54(20): 42-47.
[2]	GENG Hong, REN Daoxian, DU Peng. Dynamic parameter identification model of aircraft engine based on NARX neural network [J]. Computer Engineering and Applications, 2017, 53(12): 241-248.
[3]	SUN Lei1，2, HUANG Guoyong1，2, LI Yue1，2. Exploration of improved strong tracking SVD-UKF used in BDS/INS integrated navigation [J]. Computer Engineering and Applications, 2017, 53(10): 225-229.
[4]	ZHANG Yufeng1, ZHOU Qixun1, ZHOU Yong2, ZHANG Juzhong3. Research on adaptive square-root unsented Kalman filter for nonlinear system [J]. Computer Engineering and Applications, 2016, 52(16): 36-40.
[5]	LV Xinzhi. Theory and application of FAST-SR-UKF to integrated navigation systems [J]. Computer Engineering and Applications, 2015, 51(6): 254-259.
[6]	PENG Xiaohua1, JIA Meizhen2, WANG Lei1. Solving a class of fuzzy nonlinear systems based on homotopy method [J]. Computer Engineering and Applications, 2015, 51(18): 38-41.
[7]	LI Ying1, ZHOU Deyun1, YU Ji2. New maneuvering target tracking algorithm based on adaptive variable structure multi-model and information filtering [J]. Computer Engineering and Applications, 2015, 51(17): 217-221.
[8]	CHEN Yuchan, SHI Wuxi, CHEN Yimei. Adaptive fuzzy control of a class of uncertain non-affine nonlinear systems [J]. Computer Engineering and Applications, 2014, 50(8): 251-254.
[9]	LI Feixiang1, ZHAO Zhijin1，2, ZHAO Zhidong1. Variable step-size VLMP algorithm using divided-order gradient weighted average [J]. Computer Engineering and Applications, 2014, 50(4): 207-210.
[10]	ZHU Bo, ZHOU You, WU Guofeng, HU Hanying. AR prediction model based IMM tracking algorithm [J]. Computer Engineering and Applications, 2014, 50(24): 222-226.
[11]	WU Simin, CHEN Jun, LIU Fei. PID control of nonlinear systems based on multi-objective particle swarm optimization [J]. Computer Engineering and Applications, 2014, 50(23): 69-72.
[12]	FANG Qing, LI Ping. Unscented Kalman filter-based indoor positioning [J]. Computer Engineering and Applications, 2014, 50(14): 74-77.
[13]	YAN Pingping1, ZHAO Zhijin1，2, SHANG Junna1，2, ZHAO Zhidong1. Variable memory length LMP algorithm of second-order Volterra filter [J]. Computer Engineering and Applications, 2013, 49(3): 121-125.
[14]	LIU Suhong, LIU Chun’an. Multi-objective evolutionary algorithm for solving nonlinear system of equations [J]. Computer Engineering and Applications, 2013, 49(18): 48-51.
[15]	XU Zhicheng1, WANG Shuqing2. Parameter identification of nonlinear system model based on bacterial swarm foraging for optimization [J]. Computer Engineering and Applications, 2013, 49(1): 46-49.

Unscented Kalman filter with application to orbit determination based on space-borne GPS

UKF算法在星载GPS低轨卫星定轨中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics