带相关观测噪声系统的自校正观测融合Kalman滤波器  

Self-tuning Measurement Fusion Kalman Filter for System with Correlated Measurement Noises

作　　者：高媛[1] 邓自立[1] 

机构地区：[1]黑龙江大学自动化系,哈尔滨150080

出　　处：《科学技术与工程》2009年第7期1669-1677,共9页Science Technology and Engineering

基　　金：国家自然科学基金(60874063);黑龙江省教育厅科学技术项目(11521124);黑龙江省电子工程重点实验室项目(DZZD2006-16)资助

摘　　要：对于带有相关观测噪声、未知噪声统计、不同观测阵带有相同右因子的多传感器线性离散定常随机系统,利用相关方法,提出了噪声统计信息的在线辨识器。基于ARMA新息模型,提出了自校正加权观测融合Kalman滤波器,避免了求解Lyapunov和Riccati方程,减少了计算负担,适于实时应用。利用动态误差系统分析(DESA)方法,严格证明了提出的自校正融合滤波器以概率1或按实现收敛于相应的最优融合滤波器,即具有渐近全局最优性。一个3传感器跟踪系统的仿真例子说明其有效性。For the multisensor system with correlated measurement noises,unknown noise statistics and different measurement matrices with identical right factor,by correlated method,the online identifiers of the noise statistics are obtained.Based on ARMA innovation model,a self-tuning weighted measurement fusion Kalman filter is presented,which avoids Lyapunov and Riccati equations,reduces the computational burden and is suitable for real time application.By dynamic error system analysis（DESA） method,it is strictly proved that the proposed self-tuning fused Kalman filter converges to the corresponding optimal fused Kalman filter with probability one or in a realization,i.e.it has asymptotic global optimality.A simulation example for a target tracking systems with 3 sensors shows its effectiveness.

关 键 词：加权观测融合 自校正Kalman滤波器 收敛性 动态误差系统分析(DESA)方法 现代时间序列分析方法 

分 类 号：O211.64[理学—概率论与数理统计]