多模型信息融合Wiener状态估值器

对多模型多传感器线性离散定常随机系统,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,根据按矩阵加权、按对角阵加权和按标量加权三种最优融合规则,提出了系统公共状态的三种最优加权融合Wiener估值器。它们的精度高于每个局部估值器的精度,且可统一处理融合滤波、预报和平滑问题。为计算最优加权,提出计算局部估计误差互协方差公式。它们可用于带ARMA有色观测噪声系统状态融合滤波问题。一个跟踪系统MonteCarlo仿真例子说明其有效性。

Information Fusion Wiener State Estimators with Multi-Model

For linear discrete time-invariant stochastic systems with multi-model and multi-sensor, by the modem time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimation theory, according to three optimal fusion rules weighted by matrices, diagonal matrices and scalars, the three optimal weighted fusion Wiener estimators are presented for the common state. Their accuracy is higher than that of each local estimator, and they can handle the fused filtering, prediction, and smoothing problems in a unified framework. In order to compute the optimal weights. the formulas of computing the local estimation error cross-covariances are proposed. They can be applied to the state fused filtering problem for multisensor systems with the ARMA coloured measurement noises. A tracking system Monte Carlo simulated example shows their effectiveness.