Distributed Reduced-order Optimal Fusion Kalman Filters for Stochastic Singular Systems  

作　　者：SUN Shu-Li MA Jing 

机构地区：[1]Department of Automation, Heilongjiang University, Harbin 150080

出　　处：《自动化学报》2006年第2期286-290,共5页Acta Automatica Sinica

基　　金：Supported by National Natural Science Foundation of P. R. China (60504034) Youth Foundation of Heilongjiang Province (QC04A01) Outstanding Youth Foundation of Heilongjiang University (JC200404)

摘　　要：Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance (LMV) sense, a distributed full-order optimal fusion Kalman filter (DFFKF) is given for discrete-time stochastic singular systems with multiple sensors, which involves the inverse of a high-dimension matrix to compute matrix weights. To reduce the computational burden, a distributed reduced-order fusion Kalman filter (DRFKF) is presented, which involves in parallel the inverses of two relatively low-dimension matrices to compute matrix weights. A simulation example shows the effectiveness.Based on the optimal fusion algorithm weighted by matrices in the linear minimum variance （LMV） sense, a distributed full-order optimal fusion Kalman filter （DFFKF） is given for discrete-time stochastic singular systems with multiple sensors, which involves the inverse of a high-dimension matrix to compute matrix weights. To reduce the computational burden, a distributed reduced-order fusion Kalman filter （DRFKF） is presented, which involves in parallel the inverses of two relatively low-dimension matriccs to compute matrix weights. A simulation example shows the effectiveness.

关 键 词：多传感器 信息融合 KALMAN滤波 随机奇异系统 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]