多模型多传感器信息融合稳态白噪声反卷积估值器

对于带不同局部动态模型(多模型)的多传感器线性定常随机控制系统,应用现代时间序列分析方法,在按标量加权最优融合准则下,提出了最优信息融合稳态白噪声反卷积估值器.可统一处理白噪声反卷积融合滤波、平滑和预报问题.它的精度高于每个局部估值器的精度.为了计算最优加权,提出了局部估计误差互协方差计算公式.一个Bernoulli-Gussian白噪声反卷积融合器的仿真例子证明其有效性.

Multi-model Multi-sensor Information Fusion Steady State White Noise Deconvolution Estimators

For the multisensor linear discrete time-invariant stochastic control systems with the different local models(multi-model),using modern time series analysis method,under the optimal fusion criterion weighted by scalars,a class of the optimal information fusion white noise deconvolution estimators is presented.It can handle the white noise deconvolution fused filtering,smoothing and prediction problems in a unified framework.Its accuracy is higher than that of each local estimator.In order to compute the optimal weights,the formula of computing cross-covariances among local estimation errors is presented.A simulation example for the Bernoulli-Gussian white noise deconvalution fuser shows its effectiveness.