含未知参数的自校正融合Kalman滤波器及其收敛性  

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

作　　者：陶贵丽[1,2] 邓自立[1] 

机构地区：[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

出　　处：《自动化学报》2012年第1期109-119,共11页Acta Automatica Sinica

基　　金：国家自然科学基金(60874063);黑龙江省教育厅科学技术研究项目(11553101);黑龙江大学自动控制重点实验室项目资助~~

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.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.

关 键 词：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 

分 类 号：TN713[电子电信—电路与系统]