单通道ARMA信号信息融合Wiener反卷积滤波器

Information Fusion Wiener Deconvolution Filter for Single Channel ARMA Signals

应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、白噪声估值器和观测预报器,在线性最小方差最优加权信息融合准则下,对单通道ARMA信号提出了多传感器分布式融合Wiener反卷积滤波器,可统一处理融合滤波、平滑和预报问题。为了计算最优加权,提出了局部估计误差互协方差的计算公式。同单传感器情形相比,可提高滤波精度。一个仿真例子说明了其有效性和正确性。

Using the modern time series analysis method, based on the autoregressive moving average（ARMA） innovation model, white noise estimator and measurement predictor, under the linear minimum variance optimal fusion criterion, a unified and general multisensor distributed fusion Wiener filter is presented for single channel ARMA signals. They can handle the fused filtering, smoothing and prediction problems in a unified framework. In order to compute the optimal weights, the formula of computing the cross-covariance among local filtering errors is presented. Compared with the single sensor case, the filtering accuracy is improved. A simulation example shows its effectiveness and correctness.