Paper Info
Title
Time-Domain Approaches To Multichannel Optimal Deconvolution
Abstract
Using the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model and white noise estimators, two time-domain approaches to multichannel optimal deconvolution are presented. In the first approach, the multichannel optimal deconvolution estimators are given in the ARMA innovation filters form, where the solution of the Diophantine equations is required. Their global and local asymptotic stability is proved. In the second approach, the multichannel ARMA recursive Wiener deconvolution filters without the Diophantine equations are presented, which have asymptotic stability. The relationship between the ARMA innovation filters and ARMA Wiener deconvolution filters is discussed. Each approach can handel the deconvolution filtering, smoothing and prediction problems in a unified framework. An illustrative example and two simulation examples show their effectiveness.
Year
DOI
Venue
2000
10.1080/00207720050030824
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Keywords
Field
DocType
white noise,diophantine equation,time domain,time series analysis,moving average,asymptotic stability
Autoregressive–moving-average model,Mathematical optimization,Blind deconvolution,Filter (signal processing),Wiener deconvolution,Deconvolution,White noise,Smoothing,Mathematics,Estimator
Journal
Volume
Issue
ISSN
31
6
0020-7721
Citations 
PageRank 
References 
3
1.06
0
Authors
1
Name
Order
Citations
PageRank
Zi-li Deng151444.75