带多层融合结构的广义系统Kalman融合器  

Kalman Fuser with Multi-layer Fusion Structure for Descriptor Systems

作　　者：高媛[1] 李怀敏[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：《自动化学报》2008年第6期639-646,共8页Acta Automatica Sinica

基　　金：国家自然科学基金(60374026);黑龙江省普通高等学校电子工程重点实验室科学技术研究项目(DZZD2006-16);黑龙江省教育厅科学技术研究项目(11521214)资助~~

摘　　要：对带多传感器的线性离散随机广义系统,用奇异值分解将其化为两个降阶耦合子系统,应用现代时间序列分析方法,基于自回归滑动平均(Autoregressive moving average,ARMA)新息模型和白噪声估计理论,提出了带三层融合结构的分布式稳态Kalman融合器,它由两个加权融合器和两个复合融合器组成.第一层给出子系统状态融合器,实现了每个子系统分量解耦融合;第二层给出变换后状态融合器,实现了两个子系统的解耦融合;第三层给出原始状态融合器,它可统一处理状态融合滤波、平滑和预报问题.为计算最优加权阵,给出了计算局部估计误差互协方差阵公式,证明了它的精度比每个局部估值器精度高.Monte Carlo的仿真实例说明了其有效性.For the linear discrete stochastic descriptor system with multisensors, by using the singular value decomposition, it is transformed into two reduced order coupled subsystems. By the modern time series analysis method, based on the autoregressive moving average （ARMA） innovation model, and white noise estimation theory, a distributed steady-state Kalman fuser with a three-layer fusion structure is presented, which consists of two weighted fusers and two composite fusers. The first layer gives the state fusers of subsystems which realize a decoupled fusion of components for each subsystem. The second layer gives the transformed state fuser which realizes a decoupled fusion between two subsystems. The third layer gives the original state fuser. It can handle the fused filtering, smoothing, and prediction problems in a unified framework. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented. It is proved that its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness.

关 键 词：多传感器信息融合 广义系统 KALMAN滤波器 多层融合 解耦融合 现代时间序列分析方法 

分 类 号：TP202[自动化与计算机技术—检测技术与自动化装置]