广义系统解耦融合Wiener状态估值器

Decoupled Fusion Wiener State Estimator for Descriptor Systems

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。

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.