Based On Modern Time Series Analysis Method Of Multi-sensor Information Fusion Wiener Filter

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 information fusion criterion weighted by scalars, the multisensor information fusion single channel white noise deconvolution Wiener filter is presented. Under the optimal fusion rules weighted by matrices, diagonal matrices and scalars, the multisensor information fusion Wiener filters for multichannel ARMA signals with white observation noise and with moving average (MA) color observation noise are presented , the multichannel multisensor information fusion Wiener deconvolution filter is presented, and multisensor fusion Wiener state filter is also presented. They can handle the fused filtering , smoothing and prediction problems in a unified framework. The formulas of computing the variance and cross-covariance matrices among local estimation errors are presented, which are applied to compute the optimal weights. Compared with the single sensor case, the filtering accuracy is improved. The proposed methods, avoid the Riccati equation and Diophantine equation and can reduce the on-line computational burden. Many simulation examples for the target tracking system and numerical simulation examples show their effectiveness, and show that the accuracy distinction for three kinds of fused filters is not obvious, so that the fused filter weighed by scalars can obviously reduce the on-line computational burden, and is suitable for real time applications.