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.
出处
《自动化学报》
EI
CSCD
北大核心
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)
关键词
多传感器
信息融合
KALMAN滤波
随机奇异系统
Multisensor, information fusion, distributed reduced-order fusion filter, cross-covariance,stochastic singular system
