自校正信息融合Wiener预报器及其收敛性

对带相关观测噪声和未知噪声统计的多传感器系统,用相关方法得到噪声统计在线估值器.在按分量标量加权线性最小方差最优信息融合准则下,用现代时间序列分析方法,基于滑动平均(moving average)新息模型的辨识,提出了自校正解耦融合Wiener预报器.用动态误差系统分析(dynamic error system anallysis)方法证明了自校正融合wiener预报器收敛于最优融合Wiener预报器,因而它具有渐近最优性.它的精度比每个局部自校正Wienet预报器精度都高.它的算法简单,便于实时应用.一个目标跟踪系统的仿真例子说明了其有效性.

Self-tuning information fusion Wiener predictor and its convergence

For the multisensor systems with correlated measurement noises and unknown noise statistics.the on-line noise statistics estimators are obtained by the correlation method. Under the linear minimum variance optimal information fusion criterion weighted by scalars for components,by the modem time series analysis method.a self-tuning decoupled fusion Wiener predictor is presented based on the identification of the moving average(MA) innovation models. By using the dynamic error system analysis(DESA) method,it is proved that the self-tuning fusion Wiener predictor converges to the optimal fusion Wiener predictor,so that it has the asymptotic optimality. Its accuracy is higher than that of each local self-tuning Wiener predictor. Its algorithm is simple.and is suitable for real time applications. A simulation example for a target tracking system shows its effectiveness.