Covariance Intersection Fusion Kalman Estimators

摘要：

　　By the CI (Covariance Intersection) fusion algorithm,based on the ARMA innovation model,the two-sensor CI fusion Kalman estimators are presented for the systems with unknown cross-covariance.It is proved that their estimation accuracies are higher than those of the local Kalman estimators,and are lower than those of the optimal fused Kalman estimators.A Monte-Carlo simulation result shows that the actual accuracy of the presented CI fusion Kalman estimator are close to those of the optimal fused Kalman estimators with known cross-covariance.

关键词： Covariance intersection fusion unknown cross-covariance ARMA innovation model

作者: Yuan Gao Zili Deng

作者单位: Department of Automation, Heilongjiang University, Harbin, Heilongjiang Province, China

会议类型: 国际会议

会议名称: the Second International Conference on Frontiers of Manufacturing and Design Science(第二届制造与设计科学国际会议(ICFMD 2011))

会议地点: 台湾

会议语种:英文

页码: 750-754

在线出版日期: 2011-12-11（万方平台首次上网日期，不代表论文的发表时间）