自校正解耦融合Wiener状态预报器

对含未知模型参数和噪声统计的多传感器单输入单输出系统,用现代时间序列分析方法,基于自回归滑动平均(ARMA)新息模型的在线辨识,可得到未知模型参数和噪声统计估值器,进而在按状态分量标量加权线性最小方差最优信息融合准则下,提出了自校正分量解耦信息融合Wiener状态预报器。它实现了自校正分量解耦局部Wiener状态预报器和自校正分量解耦融合预报器。证明了它的收敛性和渐近最优性。一个目标跟踪系统的仿真例子说明了其有效性。

Self-tuning Decoupled Fusion Wiener State Predictor

For multisensor single input-single output systems with unknown model parameter and noise statistics, using the modern time series analysis method, based on the on-line identification of the autoregressive average moving (ARMA) innovation model, the estimators of unknown model parameters and noise statistics can be obtained. Further, under the minear minimum variance optimal information fusion criterion weighted by scales for state components, a self-tuning component decoupled fused Wiener state predictor is presented. It realizes the self-tuning decoupled local Wiener predictors for components and a self-tuning decoupled fusion prediction for components. Its convergence and asymptotic optimality are proved strictly. A simulation example for a target tracking system shows its effectiveness.