Based On Modern Time Series Analysis Method Of Multi-sensor Measurement Fusion Kalman Filter With The Wiener Filter

Under the linear minimum variance optimal information fusion criterion,by using the modern time series analysis method,based on the autoregressive moving average (ARMA) innovation model, by using the functional equivalence of the weighted measurement fusion and centralized measurement fusion methods, the multisensor weighted measurement fusion Kalman filter and Wiener state filter, the multisensor weighted measurement fusion single channel Wiener signal filter , the multisensor weighted measurement fusion signle channel white noise Wiener deconvolution filter and the multisensor weighted measurement fusion single channel Wiener deconvolution filter are presented respectively.Assuming that each sensor has the same measurenment matrix,compared with the centralized measurement fusion method,the weighted measurement fusion method not only it give the globally optimal estimation, but also it can obviously reduce the computational burden, so that it is suitable for real time applications.Many simulation examples show their effectiveness.