欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
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。
英文摘要：
For muhisensor single input-single output systems with unknown model parameter and noise statistics, using the modem 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 ob- tained. Further, under the linear 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.
同期刊论文项目
同项目期刊论文
期刊信息
位置：