[1]孙尧,马涛,高延滨,等.自适应扩维UKF算法在SINS/GPS组合导航系统中的应用[J].智能系统学报,2012,7(04):345-351.
 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(04):345-351.
点击复制

自适应扩维UKF算法在SINS/GPS组合导航系统中的应用(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年04期
页码:
345-351
栏目:
出版日期:
2012-08-25

文章信息/Info

Title:
An adaptive augmented unscented Kalman filter with applications in a SINS/GPS integrated navigation system
文章编号:
1673-4785(2012)04-0345-07
作者:
孙尧1马涛1高延滨1王璐2
1.哈尔滨工程大学 自动化学院,黑龙江 哈尔滨 150001;
2.上海交通大学 航空航天学院,上海 200240
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
关键词:
扩维UKF自适应渐消矩阵组合导航非线性滤波
Keywords:
augmented unscented Kalman filter adaptive fading matrix integrated navigation nonlinear filter
分类号:
TP18;U666.11
文献标志码:
A
摘要:
针对自适应渐消因子卡尔曼滤波无法应用于非线性系统的问题以及自适应渐消因子的局限性,提出了带自适应渐消矩阵的扩维UKF(adaptive fading matrix augmented UKF, AFMAUKF)算法.该算法针对含有非加性白噪声的非线性系统,引入了一种新的自适应渐消矩阵计算方法,并用Unscented变换逼近系统的后验均值和协方差,有效解决了此类系统的滤波问题.针对SINS/GPS组合导航系统的非线性状态估计问题,分别设计了滤波器容错试验和系统噪声突变试验,试验结果证明了该算法的有效性.
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.

参考文献/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.

备注/Memo

备注/Memo:
收稿日期: 2012-03-01.
网络出版日期:2012-07-12.
基金项目:国家自然科学基金资助项目(50909025/E091002);国际科技合作基金资助项目(2010DFR80140).
通信作者:马涛.
E-mail:mt_0606@yahoo.com.cn.
作者简介:
孙尧,男,1963年生,教授,博士生导师, 主要研究方向为信息融合技术、导航自动化、突变控制、精密仪器及机械、智能仪器与系统.发表学术论文多篇.
马涛,男,1984年生,博士研究生,主要研究方向为惯性导航系统、组合导航系统及技术.
高延滨,男,1963年生,教授,博士生导师,主要研究方向为微弱信号处理及噪声抑制技术、导航信息转换技术和平台及捷联式惯导系统技术,发表学术论文20余篇,出版专著1部,获得省级科技进步三等奖1项.
更新日期/Last Update: 2012-09-26