2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于EKF的集中式融合估计研究

葛泉波 李文斌 孙若愚 徐姿

葛泉波, 李文斌, 孙若愚, 徐姿. 基于EKF的集中式融合估计研究. 自动化学报, 2013, 39(6): 816-825. doi: 10.3724/SP.J.1004.2013.00816
引用本文: 葛泉波, 李文斌, 孙若愚, 徐姿. 基于EKF的集中式融合估计研究. 自动化学报, 2013, 39(6): 816-825. doi: 10.3724/SP.J.1004.2013.00816
GE Quan-Bo, LI Wen-Bin, SUN Ruo-Yu, XU Zi. Centralized Fusion Algorithms Based on EKF for Multisensor Non-linear Systems. ACTA AUTOMATICA SINICA, 2013, 39(6): 816-825. doi: 10.3724/SP.J.1004.2013.00816
Citation: GE Quan-Bo, LI Wen-Bin, SUN Ruo-Yu, XU Zi. Centralized Fusion Algorithms Based on EKF for Multisensor Non-linear Systems. ACTA AUTOMATICA SINICA, 2013, 39(6): 816-825. doi: 10.3724/SP.J.1004.2013.00816

基于EKF的集中式融合估计研究

doi: 10.3724/SP.J.1004.2013.00816
基金项目: 

国家自然科学基金(61172133, 91016020, 60934009, 61273075, 61002018)和浙江省自然科学基金(LY12F03004)资助

详细信息
    通讯作者:

    葛泉波

Centralized Fusion Algorithms Based on EKF for Multisensor Non-linear Systems

Funds: 

Supported by National Natural Science Foundation of China(61172133, 91016020, 60934009, 61273075, 61002018) and the Natural Science Foundation of Zhejiang Province(LY12F03004)

  • 摘要: 以一类非线性多传感器动态系统为对象, 基于扩展Kalman滤波器(Extend Kalman filter, EKF)介绍三种典型非线性集中式融合算法, 并以此为基础研究部分线性动态系统融合理论在非线性系统中的推广与完善. 首先,利用EKF的一种信息滤波器形式(Extend information filter, EIF)给出测量值扩维融合、测量值加权融合和顺序滤波融合算法公式, 进而研究三种非线性融合算法的估计性能比较以及测量值融合更新次序是否满足可交换性. 结果表明: 当各传感器的测量特性相同时, 集中式测量值扩维和测量值加权融合算法的估计精度功能等价;非线性顺序滤波融合与其他两种融合算法之间不再具备线性多传感器系统中估计功能的完全等价特性;在融合精度不变前提下非线性顺序滤波融合中, 各传感器观测更新次序不再完全满足可交换性. 4个基于纯方位目标跟踪的数值仿真被用来验证文中所得结论的有效性和正确性.
  • [1] Han Chong-Zhao, Zhu Hong-Yan, Duan Zhan-Sheng, Han De-Qiang, Liu Wei-Feng, Yu Xin. Multi-source Information Fusion (Second Edition). Beijing: Tsinghua University Press, 2010: 252-256 (韩崇昭, 朱洪艳, 段战胜, 韩德强, 刘伟峰, 于昕. 多源信息融合 (第二版). 北京: 清华大学出版社, 2010: 252-256)
    [2] Gan Q, Harris C J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(1): 273-279
    [3] Roecker J A, McGillem C D. Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 447-449
    [4] Yu An-Xi, Hu Wei-Dong, Zhou Wen-Hui. Performance comparison of multisensor measurement fusion algorithms. Journal of National University of Defense Technology, 2003, 25(6): 39-44 (余安喜, 胡卫东, 周文辉. 多传感器量测融合算法的性能比较. 国防科技大学学报, 2003, 25(6): 39-44)
    [5] Wen C L, Ge Q B. Asynchronous multisensor sequential data fusion based on transmission delay. In: Proceedings of the 2006 International Conference on Software and Computer Applications (ICSCA). Chongqing, China: DCDIS-B, 2006. 1386-1391
    [6] Wen C L, Ge Q B. A data fusion algorithm of the nonlinear system based on filtering step by step. International Journal of Control, Automation, and Systems, 2006, 4(2): 165-171
    [7] Wang Xiao-Xu, Liang Yan, Pan Quan, Zhao Chun-Hui, Li Han-Zhou. Unscented Kalman filter for nonlinear systems with colored measurement noise. Acta Automatica Sinica, 2012, 38(6): 986-998 (王小旭, 梁彦, 潘泉, 赵春晖, 李汉舟. 带有色量测噪声的非线性系统Unscented卡尔曼滤波器. 自动化学报, 2012, 38(6): 986-998)
    [8] Zuo Jun-Yi, Zhang Yi-Zhe, Liang Yan. Particle filter based on adaptive part resampling. Acta Automatica Sinica, 2012, 38(4): 647-652 (左军毅, 张怡哲, 梁彦. 自适应不完全重采样粒子滤波器. 自动化学报, 2012, 38(4): 647-652)
    [9] Gong Yi-Song, Gui Qing-Ming, Li Bao-Li, Wang Jun-Jiang. Adaptive fading extended Kalman particle filtering applied to integrated navigation. Journal of Geodesy and Geodynamics, 2010, 30(1): 99-103 (宫轶松, 归庆明, 李保利, 王军江. 自适应渐消扩展Kalman粒子滤波方法在组合导航中的应用. 大地测量与地球动力学, 2010, 30(1): 99-103)
    [10] Pakki K, Chandra B, Gu D W, Postlethwaite I. Cubature information filter and its applications. In: Proceedings of the 2011 American Control Conference. San Francisco, USA: IEEE, 2011. 3609-3614
    [11] Wang Z S, Zhen Z Y. Theory of nonlinear information fusion estimation. Journal of Astronautics, 2009, 30(1): 8-12
    [12] Lee D J. Nonlinear estimation and multiple sensor fusion using unscented information filtering. IEEE Signal Processing Letters, 2008, 15: 861-864
    [13] Tuna G, Gungor V C, Gulez K. GPS aided Extended Kalman filter based localization for unmanned vehicles. In: Proceedings of the 20th Signal Processing and Communications Applications Conference (SIU). Mugla, Turkey: IEEE, 2012. 1-4
    [14] Hao Gang, Ye Xiu-Fen, Chen Ting. Weighted measurement fusion algorithm for nonlinear unscented Kalman filter. Control Theory & Applications, 2011, 28(6): 753-758(郝钢, 叶秀芬, 陈亭. 加权观测融合非线性无迹卡尔曼滤波算法. 控制理论与应用, 2011, 28(6): 753-758)
    [15] Li H P, Xu D M, Zhang F B, Yao Y. Consistency analysis of EKF-based SLAM by measurement noise and observation times. Acta Automatica Sinica, 2009, 35(9): 1177-1184
    [16] Ge Q B, Li W B, Wen C L. SCKF-STF-CN: a universal nonlinear filter for maneuver target tracking. Journal of Zhejiang University——Science C, 2011, 12(8): 678-686
    [17] Cao L, Yang W W, Chen X Q, Huang Y Y. Application of multi-sensors data fusion based on improved federal filtering in micro-satellite attitude determination. In: Proceedings of the 2001 International Workshop on Multi-platform/ Multi-sensor Remote Sensing and Mapping. Xiamen, China: IEEE, 2011. 66-71
    [18] Fang J C, Gong X L. Predictive iterated Kalman filter for INS/GPS integration and its application to SAR motion compensation. IEEE Transactions on Instrumentation and Measurement, 2010, 59(4): 909-915
  • 加载中
计量
  • 文章访问数:  2340
  • HTML全文浏览量:  130
  • PDF下载量:  1667
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-10-24
  • 修回日期:  2012-09-05
  • 刊出日期:  2013-06-20

目录

    /

    返回文章
    返回