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基于Gauss-Hermite逼近的非线性加权观测融合无迹Kalman滤波器

李云 孙书利 郝钢

李云, 孙书利, 郝钢. 基于Gauss-Hermite逼近的非线性加权观测融合无迹Kalman滤波器. 自动化学报, 2019, 45(3): 593-603. doi: 10.16383/j.aas.c170534
引用本文: 李云, 孙书利, 郝钢. 基于Gauss-Hermite逼近的非线性加权观测融合无迹Kalman滤波器. 自动化学报, 2019, 45(3): 593-603. doi: 10.16383/j.aas.c170534
LI Yun, SUN Shu-Li, HAO Gang. Weighted Measurement Fusion Unscented Kalman Filter Using Gauss-Hermite Approximation for Nonlinear Systems. ACTA AUTOMATICA SINICA, 2019, 45(3): 593-603. doi: 10.16383/j.aas.c170534
Citation: LI Yun, SUN Shu-Li, HAO Gang. Weighted Measurement Fusion Unscented Kalman Filter Using Gauss-Hermite Approximation for Nonlinear Systems. ACTA AUTOMATICA SINICA, 2019, 45(3): 593-603. doi: 10.16383/j.aas.c170534

基于Gauss-Hermite逼近的非线性加权观测融合无迹Kalman滤波器

doi: 10.16383/j.aas.c170534
基金项目: 

黑龙江省省级自然科学基金 F2015014

国家自然科学基金 61503127

黑龙江省高等教育机构科技创新研究队伍 2012TD007

黑龙江省普通高等学校长江学者后备支持计划 2013CJHB005

国家自然科学基金 61573132

详细信息
    作者简介:

    李云    哈尔滨商业大学计算机与信息工程学院副教授.黑龙江大学电子工程学院博士研究生.主要研究方向为状态估计, 多传感器信息融合.E-mail:liyunhd@sina.com

    郝钢    黑龙江大学电子工程学院副教授.主要研究方向为状态估计, 多传感器信息融合.E-mail:haogang@hlju.edu.cn

    通讯作者:

    孙书利    黑龙江大学电子工程学院教授.主要研究方向为网络系统滤波, 多传感器信息融合.本文通信作者.E-mail:sunsl@hlju.edu.cn

Weighted Measurement Fusion Unscented Kalman Filter Using Gauss-Hermite Approximation for Nonlinear Systems

Funds: 

Natural Science Foundation of Heilongjiang Province F2015014

Natural Science Foundation of China 61503127

Science and Technology Innovative Research Team in Higher Educational Institutions of Heilongjiang Province 2012TD007

Chang Jiang Scholar Candidates Program for Provincial Universities in Heilongjiang 2013CJHB005

Natural Science Foundation of China 61573132

More Information
    Author Bio:

    Associate professor at the School of Computer and Information Engineering, Harbin University of Commerce. Ph. D. candidate at the School of Electronic Engineering, Heilongjiang University. Her research interest covers the state estimation and multi-sensor fusion

    Associate professor at the School of Electronic Engineering, Heilongjiang University. His research interest covers the state estimation and multi-sensor fusion

    Corresponding author: SUN Shu-Li Professor at the School of Electronic Engineering, Heilongjiang University. His research interest covers the networked systems flltering and multi-sensor fusion. Corresponding author of this paper
  • 摘要: 对非线性多传感器系统,基于Gauss-Hermite逼近方法和加权最小二乘法,提出了一种具有普适性的非线性加权观测融合算法.该算法可将一个高维观测压缩为一个低维观测.在此基础上,结合无迹Kalman滤波器(Unscented Kalman filter,UKF),提出了非线性加权观测融合无迹Kalman滤波器(WMF(Weighted measurement fusion)-UKF).与集中式融合UKF(CMF(Centralized measurement fusion)-UKF)相比,该算法计算负担小且具有逼近的估计精度.特别是在传感器数量较大时,该算法在计算量上的优势更加明显.仿真例子验证了算法的有效性.
    1)  本文责任编委 李鸿一
  • 图  1  真实状态及WMF-UKF估计曲线

    Fig.  1  Curves of the true state and the WMF-UKF estimate

    图  2  局部UKF, WMF-UKF以及CMF-UKF的AMSE曲线

    Fig.  2  AMSE curves of local UKF, WMF-UKF and CMF-UKF

    图  3  加权系数矩阵$\overline{M}$和$\overline{H}^{(\rm{I})}$的计算

    Fig.  3  Calculation of the weighted matrices $\overline{M}$ and $\overline{H}^{(\rm{I})}$

    图  4  真实轨迹和WMF-UKF, 8-CMF-UKF和5-CMF-UKF的估计曲线

    Fig.  4  True and estimated tracks using WMF-UKF, 8-CMF-UKF and 5-CMF-UKF

    图  5  位置融合估计的AMSE曲线

    Fig.  5  AMSE curves of position fusion estimates

    图  6  带不同Hermite多项式的WMF-UKF位置AMSE曲线

    Fig.  6  AMSE curves of WMF-UKFs with different Hermite polynomials for position

  • [1] 韩崇昭, 朱洪艳.多传感信息融合与自动化.自动化学报, 2002, 28(S1):117-124 http://www.aas.net.cn/CN/abstract/abstract14371.shtml

    Han Chong-Zhao, Zhu Hong-Yan. Multiple-sensor information fusion and automation. Acta Automatica Sinica, 2002, 28(S1):117-124 http://www.aas.net.cn/CN/abstract/abstract14371.shtml
    [2] 潘泉, 于昕, 程咏梅, 张洪才.信息融合理论的基本方法与进展.自动化学报, 2003, 29(4):599-615 http://www.aas.net.cn/CN/abstract/abstract13929.shtml

    Pan Quan, Yu Xin, Cheng Yong-Mei, Zhang Hong-Cai. Essential methods and progress of information fusion theory. Acta Automatica Sinica, 2003, 29(4):599-615 http://www.aas.net.cn/CN/abstract/abstract13929.shtml
    [3] Sun S L, Deng Z L. Multi-sensor optimal information fusion Kalman filter. Automatica, 2004, 40(6):1017-1023 doi: 10.1016/j.automatica.2004.01.014
    [4] 邓自立, 郝钢.自校正多传感器观测融合Kalman估值器及其收敛性分析.控制理论与应用, 2008, 25(5):845-852 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy200805009

    Deng Zi-Li, Hao Gang. Self-tuning multisensor measurement fusion Kalman estimator and its convergence analysis. Control Theory & Applications, 2008, 25(5):845-852 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy200805009
    [5] Ran C J, Deng Z L. Self-tuning weighted measurement fusion Kalman filtering algorithm. Computational Statistics & Data Analysis, 2012, 56(6):2112-2128 http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0226056500/
    [6] Li X R, Jilkov V P. A survey of maneuvering target tracking:approximation techniques for nonlinear filtering. In:Proceedings of the 2004 SPIE Conference on Signal and Data Processing of Small Targets. Orlando, USA:SPIE, 2004. 537-550
    [7] Bar-Shalom Y, Li X R. Multitarget-Multisensor Tracking:Principles and Techniques. Storrs:YBS Publishing, 1995. 87-99
    [8] Sun S L. Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor. Automatica, 2004, 40(8):1447-1453 doi: 10.1016/j.automatica.2004.03.012
    [9] Sun S L. Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother. Automatica, 2005, 41(12):2153-2159 doi: 10.1016/j.automatica.2005.06.014
    [10] Gan Q 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 doi: 10.1109/7.913685
    [11] Julier S J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems. In:Proceedings of the 11th International Symposium on Aerospace/Defence Sensing, Simulation and Controls. Orlando, USA:IEEE, 1997. 182-193
    [12] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3):401-422 http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs-e200801002
    [13] Arasaratnam I, Haykin S. Cubature Kalman filters. IEEE Transactions on Automatic Control, 2009, 54(6):1254-1269 doi: 10.1109/TAC.2009.2019800
    [14] Ge Q B, Shao T, Yang Q M, Shen X F, Wen C L. Multisensor nonlinear fusion methods based on adaptive ensemble fifth-degree iterated cubature information filter for biomechatronics. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2016, 46(7):912-925 doi: 10.1109/TSMC.2016.2523911
    [15] Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2):174-188 doi: 10.1109/78.978374
    [16] Lan J, Li X R. Nonlinear estimation by LMMSE-based estimation with optimized uncorrelated augmentation. IEEE Transactions on Signal Processing, 2015, 63(16):4270-4283 doi: 10.1109/TSP.2015.2437834
    [17] Assa A, Janabi-Sharifi F. A Kalman filter-based framework for enhanced sensor fusion. IEEE Sensors Journal, 2015, 15(6):3281-3292 doi: 10.1109/JSEN.2014.2388153
    [18] Straka O, Duník J, Šimandl M. Truncation nonlinear filters for state estimation with nonlinear inequality constraints. Automatica, 2012, 48(2):273-286 doi: 10.1016/j.automatica.2011.11.002
    [19] Ge Q B, Xu D X, Wen C L. Cubature information filters with correlated noises and their applications in decentralized fusion. Signal Processing, 2014, 94:434-444 doi: 10.1016/j.sigpro.2013.06.015
    [20] Hlinka O, Sluciak O, Hlawatsch F, Djuric P M, Rupp M. Likelihood consensus and its application to distributed particle filtering. IEEE Transactions on Signal Processing, 2012, 60(8):4334-4349 doi: 10.1109/TSP.2012.2196697
    [21] 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 http://www.aas.net.cn/CN/abstract/abstract18107.shtml

    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 http://www.aas.net.cn/CN/abstract/abstract18107.shtml
    [22] Jia B, Xin M, Cheng Y. High-degree cubature Kalman filter. Automatica, 2013, 49(2):510-518 doi: 10.1016/j.automatica.2012.11.014
    [23] Khaleghi B, Khamis A, Karray F O, Razavi S N. Multisensor data fusion:a review of the state-of-the-art. Information Fusion, 2013, 14:28-44 doi: 10.1016/j.inffus.2011.08.001
    [24] 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 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201106001

    Hao Gang, Ye Xiu-Fen, Chen Ting. Weighted measurement fusion algorithm for nonlinear unscented Kalman filter. Control Theory & Applications, 2011, 28(6):753-758 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201106001
    [25] Hao G, Sun S L, Li Y. Nonlinear weighted measurement fusion unscented Kalman filter with asymptotic optimality. Information Sciences, 2015, 299:85-98 doi: 10.1016/j.ins.2014.12.013
    [26] Pomorski K. Gauss-Hermite approximation formula. Computer Physics Communications, 2006, 174(3):181-186 doi: 10.1016/j.cpc.2005.09.009
    [27] Strutinsky V M. "Shells" in deformed nuclei. Nuclear Physics A, 1968, 122(1):1-33 doi: 10.1016-0375-9474(68)90699-4/
    [28] Strutinsky V M. Shell effects in nuclear masses and deformation energies. Nuclear Physics A, 1967, 95(2):420-442 doi: 10.1016/0375-9474(67)90510-6
    [29] Oussalah M, Messaoudi Z, Ouldali A. Track-to-track measurement fusion architectures and correlation analysis. Journal of Universal Computer Science, 2010, 16(1):37-61 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=Open J-Gate000002511857
    [30] Santhanam B, Santhanam T S. On discrete Gauss-Hermite functions and eigenvectors of the discrete Fourier transform. Signal Processing, 2008, 88(11):2738-2746 doi: 10.1016/j.sigpro.2008.05.016
    [31] Ge Q B, Shao T, Chen S D, Wen C L. Carrier tracking estimation analysis by using the extended strong tracking filtering. IEEE Transactions on Industrial Electronics, 2017, 64(2):1415-1424 doi: 10.1109/TIE.2016.2610403
    [32] Du D J, Chen R, Fei M R, Li K. A novel networked online recursive identification method for multivariable systems with incomplete measurement information. IEEE Transactions on Signal and Information Processing over Networks, 2017, 3(4):744-759 doi: 10.1109/TSIPN.2017.2662621
    [33] 杜大军, 商立立, 漆波, 费敏锐.一种不完全信息下递推辨识方法及收敛性分析.自动化学报, 2015, 41(8):1502-1515 http://www.aas.net.cn/CN/abstract/abstract18724.shtml

    Du Da-Jun, Shang Li-Li, Qi Bo, Fei Min-Rui. Convergence analysis of an online recursive identification method with uncomplete communication constraints. Acta Automatica Sinica, 2015, 41(8):1502-1515 http://www.aas.net.cn/CN/abstract/abstract18724.shtml
    [34] 王小旭, 赵琳, 夏全喜, 曹伟, 李亮.噪声相关条件下Unscented卡尔曼滤波器设计.控制理论与应用, 2010, 27(10):1362-1368 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201010011

    Wang Xiao-Xu, Zhao Lin, Xia Quan-Xi, Cao Wei, Li Liang. Design of unscented Kalman filter with correlative noises. Control Theory & Applications, 2010, 27(10):1362-1368 http://d.old.wanfangdata.com.cn/Periodical/kzllyyy201010011
    [35] 王小旭, 赵琳, 潘泉, 夏全喜, 洪伟.基于最小均方误差估计的噪声相关UKF设计.控制与决策, 2010, 25(9):1393-1398 http://d.old.wanfangdata.com.cn/Periodical/kzyjc201009023

    Wang Xiao-Xu, Zhao Lin, Pan Quan, Xia Quan-Xi, Hong Wei. Design of UKF with correlative noises based on minimum mean square error estimation. Control and Decision, 2010, 25(9):1393-1398 http://d.old.wanfangdata.com.cn/Periodical/kzyjc201009023
    [36] Fang D L, Ran X M. A distributed sensor management algorithm based on auction. Procedia Computer Science, 2017, 107:618-623 doi: 10.1016/j.procs.2017.03.165
    [37] 张召友, 郝燕玲, 吴旭. 3种确定性采样非线性滤波算法的复杂度分析.哈尔滨工业大学学报, 2013, 45(12):111-115 http://d.old.wanfangdata.com.cn/Periodical/hebgydxxb201312020

    Zhang Zhao-You, Hao Yan-Ling, Wu Xu. Complexity analysis of three deterministic sampling nonlinear filtering algorithms. Journal of Harbin Institute of Technology, 2013, 45(12):111-115 http://d.old.wanfangdata.com.cn/Periodical/hebgydxxb201312020
    [38] Kitagawa G. Non-Gaussian state space modeling of time series. In:Proceedings of the 26th Conference on Decision and Control. Los Angeles, USA:IEEE, 1987. 1700-1705
    [39] Cheng P, Yang Y, Oelmann B. Stator-free RPM sensor using accelerometers-a statistical performance simulation by Monte Carlo method. IEEE Sensors Journal, 2011, 11(12):3368-3376 doi: 10.1109/JSEN.2011.2159108
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  • 收稿日期:  2017-09-21
  • 录用日期:  2018-03-16
  • 刊出日期:  2019-03-20

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