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

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

陶贵丽^1,2,
邓自立^1, ,

1.
黑龙江大学自动化系哈尔滨 150080;
2.
黑龙江科技学院计算机与信息工程学院哈尔滨 150027

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

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出版历程
- 收稿日期: 2010-11-17
- 修回日期: 2011-09-07
- 刊出日期: 2012-01-20

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

TAO Gui-Li^1,2,
DENG Zi-Li^{1
, ,}

1.
Department of Automation, Heilongjiang University, Harbin 150080;
2.
Computer and Information Engineering College, Heilongjiang Institute of Science and Technology, Harbin 150027

摘要

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.
- 多传感器信息融合 /
- 自校正融合 /
- 偏差补偿最小二乘法 /
- 收敛性 /
- 动态误差系统分析方法 /
- Kalman滤波器
Abstract: For the multisensor systems with unknown model parameters and noise variances, a self-tuning decoupled fused Kalman filter is presented based on the optimal fusion rule weighted by scalars for components. Its convergence is proved by using the dynamic error system analysis (DESA) method. As an application to signal processing, the multidimensional and multiple bias compensated recursive least-squares (BCRLS) algorithms for estimating the AR parameters are presented for the multisensor multidimensional autoregressive (AR) signal with white and colored measurement noises. The equivalence between the two BCRLS algorithms is proved. The convergence of the two BCRLS algorithms is proved by DESA method. Further more, a self-tuning fused Kalman filter for the AR signal is presented, which has asymptotic optimality. A simulation example shows the effectiveness.
- Multisensor information fusion /
- self-tuning fusion /
- bias compensated least-squares (BCRLS) method /
- convergence /
- dynamic error system analysis (DESA) method /
- Kalman filter

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参考文献(1)

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