Time-domain approaches to multichannel optimal deconvolution

Using the modern time series analysis method, based on the autoregressive m oving average (ARMA) innovation model and white noise estimators, two time- domain approaches to multichannel optimal deconvolution are presented. In t he first approach, the multichannel optimal deconvolution estimators are gi ven in the ARMA innovation filters form, where the solution of the Diophant ine equations is required. Their global and local asymptotic stability is p roved. In the second approach, the multichannel ARMA recursive Wiener decon volution filters without the Diophantine equations are presented, which hav e asymptotic stability. The relationship between the ARMA innovation filter s and ARMA Wiener deconvolution filters is discussed. Each approach can han del the deconvolution filtering, smoothing and prediction problems in a uni fied framework. An illustrative example and two simulation examples show th eir effectiveness.