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含未知参数的自校正融合Kalman滤波器及其收敛性

陶贵丽 邓自立

陶贵丽, 邓自立. 含未知参数的自校正融合Kalman滤波器及其收敛性. 自动化学报, 2012, 38(1): 109-119. doi: 10.3724/SP.J.1004.2012.00109
引用本文: 陶贵丽, 邓自立. 含未知参数的自校正融合Kalman滤波器及其收敛性. 自动化学报, 2012, 38(1): 109-119. doi: 10.3724/SP.J.1004.2012.00109
TAO Gui-Li, DENG Zi-Li. Self-tuning Fusion Kalman Filter with Unknown Parameters and Its Convergence. ACTA AUTOMATICA SINICA, 2012, 38(1): 109-119. doi: 10.3724/SP.J.1004.2012.00109
Citation: TAO Gui-Li, DENG Zi-Li. Self-tuning Fusion Kalman Filter with Unknown Parameters and Its Convergence. ACTA AUTOMATICA SINICA, 2012, 38(1): 109-119. doi: 10.3724/SP.J.1004.2012.00109

含未知参数的自校正融合Kalman滤波器及其收敛性

doi: 10.3724/SP.J.1004.2012.00109
详细信息
    通讯作者:

    邓自立 黑龙江大学自动化系教授. 主要研究方向为最优和自校正滤波,状态估计,多传感器信息融合和信号处理. 本文通信作者. E-mail: dzl@hlju.edu.cn

Self-tuning Fusion Kalman Filter with Unknown Parameters and Its Convergence

  • 摘要: 对于带未知模型参数和噪声方差的多传感器系统,基于分量按标量加权最优融合准则,提出了自校正解耦融合Kalman滤波器,并应用动态误差系统分析(Dynamic error system analysis,DESA)方法证明了它的收敛性.作为在信号处理中的应用,对带有色和白色观测噪声的多传感器多维自回归(Autoregressive,AR)信号,分别提出了AR信号模型参数估计的多维和多重偏差补偿递推最小二乘(Bias compensated recursive least-squares,BCRLS)算法,证明了两种算法的等价性,并且用DESA方法证明了它们的收敛性.在此基础上提出了AR信号的自校正融合Kalman滤波器,它具有渐近最优性.仿真例子说明了其有效性.
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  • 收稿日期:  2010-11-17
  • 修回日期:  2011-09-07
  • 刊出日期:  2012-01-20

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