【摘要】 应用现代时间序列分析方法,基于自回归滑动平均(ARMA)新息模型和白噪声估计理论,在线性最小方差分量标量加权最优信息融合准则下,提出了多传感器广义线性离散随机系统分量解耦融合Wiener状态估值器,可统一处理融合滤波、预报和平滑问题,可处理非因果广义系统。为了计算最优加权,给出了计算局部估计误差互协方差阵公式。它的精度比每个局部估值器精度高。一个MonteCarlo仿真例子说明其有效性。更多还原

【Abstract】 By the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimation theory, using the optimal fusion rule weighted by scalars for components, a component decoupled fusion Wiener state estimator is presented for the linear discrete stochastic descriptor systems with multisensor. The fused filtering, smoothing, and prediction problems can be handled in a unified framework and can handle non-cause decriptor system. In order to compute the optimal weights, the formula of computing the cross-covariances among local estimation errors is presented. Its accuracy is higher than that of each local estimator. A Monte Carlo simulation example shows its effectiveness.更多还原