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中文摘要：
对含未知噪声方差阵的多传感器系统,用现代时间序列分析方法,基于滑动平均新息模型的在线辨识和求解相关函数矩阵方程组,可得到白噪声方差阵的在线估值器。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,提出了一种自校正解耦信息融合Wiener状态预报器,实现了状态分量的自校正解耦局部Wiener预报器和自校正解耦融合Wiener预报器。用动态误差系统稳定性分析方法证明了该预报器的收敛性,即若滑动平均新息模型参数估计是一致的,将收敛于噪声方差阵已知时的最优解耦信息融合Wiener状态预报器。一个带三传感器的目标跟踪系统的仿真例子说明了其有效性。
英文摘要：
For the multisensor systems with unknown noise variance matrices, using the modern time series analysis method, based on on-line identification of the moving average （MA） innovation models, and based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variance matrices can be obtained, and further under the linear minimum variance optimal information fusion criterion weighted by scalars for state components, a self-tuning decoupled information fusion Wiener state predictor is presented. It realizes the self-tuning decoupled local Wiener predictors and self-tuning decoupled fused Wiener predictors for the state components. Its convergence is proved by the stability analysis method of dynamic error system, i. e. if the parameter estimation of moving average innovation model is consistent, then it will converge to the optimal decoupled information fusion Wiener state predictor with known noise variance matrices. A simulation example for a target tracking system with 3-sensor shows its effectiveness.
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