[1]SUN Yao,MA Tao,GAO Yanbin,et al.An adaptive augmented unscented Kalman filter with applications in a SINS/GPS integrated navigation system[J].CAAI Transactions on Intelligent Systems,2012,7(4):345-351.
Copy

An adaptive augmented unscented Kalman filter with applications in a SINS/GPS integrated navigation system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 7 Number of periods: 2012 4 Page number: 345-351 Column: 学术论文—智能系统 Public date: 2012-08-25
Title:
An adaptive augmented unscented Kalman filter with applications in a SINS/GPS integrated navigation system
Author(s):
SUN Yao1;  MA Tao1;  GAO Yanbin1;  WANG Lu2
1. College of Automation, Harbin Engineering University，Harbin 150001, China；
2. School of Aeronautics and Astronautics, Shanghai JiaoTong University, Shanghai 200240, China
Keywords:
augmented unscented Kalman filter;  adaptive fading matrix;  integrated navigation;  nonlinear filter
CLC:
TP18；U666.11
DOI:
-
Abstract:
Because an adaptive fading Kalman filter cannot be applied to nonlinear systems, an augmented unscented Kalman filter (AUKF) based on an adaptive fading matrix (AFM) was proposed in this paper. The AFMAUKF algorithm was implemented by first calculating the adaptive fading matrix, and then using the unscented transformation to estimate the posterior mean and covariance of the state of a nonlinear system, so as to effectively solve the filtering problem. In order to solve the problem of nonlinear state estimation in a lowcost integrated navigation system, a filter faulttolerant experiment and a system noise mutation experiment were designed and implemented, respectively. The experimental results prove that the algorithm enhances the robustness of the filter when the system model is uncertain, improves the accuracy of the filter, and has a strong faulttolerant ability.
References:
［1］CHUI C K， CHEN G. Kalman filtering with realtime applications［M］. 4th ed. New York: SpringerVerlag, 2009:2028.
［2］HAJIYEV C. Adaptive filtration algorithm with the filter gain correction applied to integrated INS/Radar altimeter［J］. Journal of Aerospace Engineering, 2007, 221(5): 847855.
［3］耿延睿, 崔中兴, 张洪钺, 等. 衰减因子自适应滤波及在组合导航中的应用［J］. 北京航空航天大学学报, 2004, 30(5): 434437.
 GENG Yanrui, CUI Zhongxin, ZHANG Hongyue, et al. Adaptive fading Kalman filter with applications in integrated navigation system［J］. Journal of Beijing University of Aeronautics and Astronautics, 2004, 30(5): 434437.
［4］夏启军, 孙优贤, 周春晖. 渐消卡尔曼滤波器的最佳自适应算法及其应用［J］. 自动化学报, 1990, 16(3): 210216. 
XIA Qijun, SUN Youxian, ZHOU Chunhui. An optimal adaptive algorithm for fading Kalman filter and its application［J］. Acta Automatica Sinica, 1990, 16(3): 210216.
［5］高青伟,赵国荣,吴芳,等.衰减记忆自适应滤波在惯导系统传递对准中的应用［J］. 系统工程与电子技术, 2010, 32(12): 26482651. 
GAO Qingwei, ZHAO Guorong, WU Fang, et al. Application of adaptive fading filter technique in transfer alignment of inertial navigation systems［J］. Systems Engineering and Electronics, 2010, 32(12): 26482651.
［6］JULIER S, UHLMANN J K, DURRANTWHYTE H F. A new approach for filtering nonlinear system［C］//Proceedings of the American Control Conference. Seattle, USA, 1995:16281632.
［7］MERWE R. Sigmapoint Kalman filters for probabilistic inference in dynamic statespace models［D］. Portland,USA： Oregon Health and Science University, 2004: 108125.
［8］BRIERS M, MASKELL S R, WRIGHT R. A RaoBlackwellised unscented Kalman filter［C］//Proceedings of the 6th International Conference of Information Fusion. Cairns,Australia， 2003: 5561.
［9］王小旭, 赵琳, 夏全喜, 等. 基于Unscented变换的强跟踪滤波器［J］. 控制与决策, 2010, 24(7): 10631068. 
WANG Xiaoxu, ZHAO Lin, XIA Quanxi, et al. Strong tracking filter based on unscented transformation［J］. Control and Decision, 2010, 24(7): 10631068.
［10］霍成立, 谢凡, 秦世引. 面向室内移动机器人的无迹滤波实时导航方法［J］. 智能系统学报, 2009, 4(4): 295302.
 HUO Chengli, XIE Fan, QIN Shiyin. A case study in realtime UKFbased navigation for indoor autonomous travel of mobile robots［J］. CAAI Transactions on Intelligent Systems, 2009, 4(4): 295302.
［11］Van der MERWE R, WAN E A. Sigmapoint Kalman filters for integrated navigation［C］//Proceedings of the 60th Annual Meeting of The Institute of Navigation. Dayton, USA, 2004： 641654.
Similar References:

Memo

-

Last Update: 2012-09-26

Copyright © CAAI Transactions on Intelligent Systems