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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

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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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