Information Fusion White Noise Deconvolution Estimators for Multisensor Systems with Different Local Models

摘要：

White noise deconvolution or input white noise estimation problem has important applications in oil seismic exploration, communication, and signal processing. Based on the optimal information fusion rules, in the linear minimum variance sense, three distributed optimal fused white noise deconvolution estimators weighted by matrices, diagonal matrices and scalars, are presented for the linear discrete time-varying stochastic systems with multisensor and different local dynamic models, respectively. The accuracy of the fusers is higher than that of each local white noise estimator. They can handle the white noise fused filtering, prediction and smoothing problems, and are applicable to the systems with colored measurement noise. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is presented. A Monte Carlo simulation example for the Bernoulli-Gaussian input white noise shows their effectiveness.

关键词： Multisensor Information Fusion Time-varying System Deconvolution White Noise Estimator Kalman Filtering Method

作者: Xiao-Jun Sun Zi-Li Deng

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

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 1044-1049

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）