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提高测量可靠性的多传感器数据融合有偏估计方法

岳元龙 左信 罗雄麟

岳元龙, 左信, 罗雄麟. 提高测量可靠性的多传感器数据融合有偏估计方法. 自动化学报, 2014, 40(9): 1843-1852. doi: 10.3724/SP.J.1004.2014.01843
引用本文: 岳元龙, 左信, 罗雄麟. 提高测量可靠性的多传感器数据融合有偏估计方法. 自动化学报, 2014, 40(9): 1843-1852. doi: 10.3724/SP.J.1004.2014.01843
YUE Yuan-Long, ZUO Xin, LUO Xiong-Lin. Improving Measurement Reliability with Biased Estimation for Multi-sensor Data Fusion. ACTA AUTOMATICA SINICA, 2014, 40(9): 1843-1852. doi: 10.3724/SP.J.1004.2014.01843
Citation: YUE Yuan-Long, ZUO Xin, LUO Xiong-Lin. Improving Measurement Reliability with Biased Estimation for Multi-sensor Data Fusion. ACTA AUTOMATICA SINICA, 2014, 40(9): 1843-1852. doi: 10.3724/SP.J.1004.2014.01843

提高测量可靠性的多传感器数据融合有偏估计方法

doi: 10.3724/SP.J.1004.2014.01843
基金项目: 

国家重点基础研究发展计划(973计划)(2012CB720500),国家自然科学基金(21006127,61104218),中国石油大学(北京)科研基金资助项目(YJRC-2013-12)资助

详细信息
    作者简介:

    岳元龙 中国石油大学(北京)自动化系博士生.主要研究方向为测量、估计与可靠性.

    通讯作者:

    罗雄麟 中国石油大学(北京)自动化系教授.主要研究方向为过程控制与实时优化,机器学习与智能控制,预测控制,过程系统工程.本文通信作者.E-mail:luoxl@cup.edu.cn

Improving Measurement Reliability with Biased Estimation for Multi-sensor Data Fusion

Funds: 

Supported by the National Basic Research Program of China (973 Program) (2012CB720500), National Natural Science Foundation of China (21006127, 61104218), and the Science Foundation of China University of Petroleum (YJRC-2013-12)

  • 摘要: 为了提高测量数据可靠性,多传感器数据融合在过程控制领域得到了广泛应用. 本文基于有偏估计能够减小最小二乘无偏估计方差的思想,提出采用多传感器有偏估计数据融合改善测量数据可靠性的方法. 首先,基于岭估计提出了有偏测量过程,并给出了测量数据可靠性定量表示方法,同时证明了有偏测量可靠度优于无偏测量可靠度. 其次,提出了多传感器有偏估计数据融合方法,证明了现有集中式与分布式无偏估计数据融合之间的等价性. 最后,证明了多传感器有偏估计数据融合收敛于无偏估计数据融合. 实例应用验证了方法的有效性.
  • [1] Zhang Zheng-Jiang, Zeng Guo-Qiang, Shao Zhi-Jiang, Wang Ke-Xin, Chen Xi. Methodology of data reconciliation and parameter estimation for variable load in process system. CIESC Journal, 2012, 63(6): 1780-1789 (张正江, 曾国强, 邵之江, 王可心, 陈曦. 过程系统变负荷下的数据校正与参数估计方法. 化工学报, 2012, 63(6): 1780-1789)
    [2] Song Hai-Hua, Wang Xiu-Li, Li Hong-Hai. Measurement and prediction of interfacial area on distillation tray. CIESC Journal, 2003, 54(8): 1112-1117 (宋海华, 王秀丽, 李红海. 精馏塔板上气液相界面积的测量与预测. 化工学报, 2003, 54(8): 1112-1117)
    [3] E Jia-Qiang, Wang Yao-Nan, Mei Chi. Soft-sensing model of copper liquid temperature in copper refining process and its application. CIESC Journal, 2006, 57(1): 203-209 (鄂加强, 王耀南, 梅炽. 铜精炼过程铜液温度软测量模型及应用. 化工学报, 2006, 57(1): 203-209)
    [4] Viera A J, Garrett J M. Understanding inter-observer agreement: the kappa statistic. Family Medicine, 2005, 37(5): 360-363
    [5] Ge Quan-Bo, Li Wen-Bin, Sun Ruo-Yu, Xu Zi. Centralized fusion algorithms based on EKF for multisensor. Acta Automatica Sinca, 2013, 39(6): 816-825 (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)
    [6] Deng Z L, Zhang P, Qi W J, Liu J F, Gao Y. Sequential covariance intersection fusion Kalman filter. Information Sciences, 2012, 189: 293-309
    [7] Carlson N A. Federated square root filter for decentralized parallel processes. IEEE Transactions on Aerospace and Electronic Systems, 1990, 26(3): 517-525
    [8] Wen C B, Cai Y Z, Wen C L, Xu X M. Optimal sequential Kalman filtering with cross-correlated measurement noises. Aerospace Science and Technology, 2013, 26(1): 153-159
    [9] Feng J X, Wang Z D, Zeng M. Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises. Information Fusion, 2013, 14(1): 78-86
    [10] Sun S L, Deng Z L. Multi-sensor optimal information fusion Kalman filter. Automatica, 2004, 40(6): 1017-1023
    [11] Ran C J, Deng Z L. Self-tuning weighted measurement fusion Kalman filtering algorithm. Computational Statistics & Data Analysis, 2012, 56(6): 2112-2128
    [12] Mănsson K. On ridge estimators for the negative binomial regression model. Economic Modelling, 2012, 29(2): 178-184
    [13] 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
    [14] Stein C. Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. In: Proceedings of the 3rd Berkeley Symposium on Mathematical Statistics and Probability. 1956, 1: 197-206
    [15] James W, Stein C. Estimation with quadratic loss. In: Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability. 1961, 1: 311-319
    [16] Sclove S L. Improved Estimators for Coefficients in Linear Regression. Technical Report, Department of Statistics Stanford University, 1967, No.128
    [17] Liu K. Using Liu-type estimator to combat collinearity. Communications in Statistics Theory and Methods, 2003, 32(5): 1009-1020
    [18] Duran E A, Akdeniz F. Efficiency of the modified jackknifed Liu-type estimator. Statistical Papers, 2012, 53(2): 265-280
    [19] Hoerl A E, Kennard R W. Ridge regression: biased estimation for non-orthogonal problems. Technometrics, 1970, 12(1): 55-67
    [20] Zhen Zi-Yang, Wang Zhi-Sheng, Wang Dao-Bo. Predictive control based on information fusion optimal estimation for nonlinear discrete system. Acta Automatica Sinca, 2008, 34(3): 331-336 (甄子洋, 王志胜, 王道波. 基于信息融合最优估计的非线性离散系统预测控制. 自动化学报, 2008, 34(3): 331-336)
    [21] Du P J, Liu S C, Xia J S, Zhao Y D. Information fusion techniques for change detection from multi-temporal remote sensing images. Information Fusion, 2013, 14(1): 19-27
    [22] Feng Xiao-Liang, Wen Cheng-Lin, Liu Wei-Feng, Li Xiao-Fang, Xu Li-Zhong. Sequential fusion finite horizon H∞ filtering for multisenor system. Acta Automatica Sinca, 2013, 39(9): 1523-1532 (冯肖亮, 文成林, 刘伟峰, 李晓芳, 徐立中. 基于多传感器的序贯式融合有限域H∞ 滤波方法. 自动化学报, 2013, 39(9): 1523-1532)
    [23] Luo Ben-Cheng, Yuan Kui, Chen Jin-Long, Zhu Hai-Bin. Uncertainty analysis based dynamic multi-sensor data fusion. Acta Automatica Sinica, 2004, 30(3): 407-415 (罗本成, 原魁, 陈晋龙, 朱海滨. 一种基于不确定分析的多传感器信息动态融合方法. 自动化学报, 2004, 30(3): 407-415)
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出版历程
  • 收稿日期:  2013-05-31
  • 修回日期:  2014-02-26
  • 刊出日期:  2014-09-20

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