Multisensor information fusion Wiener state estimators for descriptor systems

摘要：

By the modern time-series analysis method,based on autoregressive moving average (ARMA)innovation model and white noise estimation theory,using the optimal fusion rule weighted by diagonal matrices, a distributed decoupled descriptor Wiener state fuser is presented for the linear discrete stochastic descriptor systems with multisensor. It realizes a decoupled fusion estimation for state components. The descriptor Wiener state fuser is obtained by weighting the local Wiener state estimators. In order to compute the optimal weights, the formulas of computing the cross-covariances among local estimation errors are presented. It can handle the fused filtering, smoothing,and prediction problems in a unified framework. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness.

关键词： Multisensor information fusion weighted fusion decoupled fusion descriptor system Wiener state fuser ARMA innovation model modern time series analysis method

作者: Yuan Gao Lixin Yang Chenjian Ran Zili Deng

作者单位: Department of Automation Heilongjiang University 150080, Harbin, China

会议类型: 国际会议

会议名称: The International Colloquium on Onformation Fusion 2007(2007年国际信息融合研讨会)

会议地点: 西安

会议语种:英文

页码: 110-117

在线出版日期: 2007-08-22（万方平台首次上网日期，不代表论文的发表时间）