Optimal reconstruction of ARMA signals with decimated samples under corrupting noise by use of multirate Kalman filter
type: Item
instance of: scholarly article
title: Optimal reconstruction of ARMA signals with decimated samples under corrupting noise by use of multirate Kalman filter
zbMATH Open document ID: 0982.94500
DOI: 10.1007/BF01204684
author: You-Li Chen
author: Bor-Sen Chen
published in: Circuits, Systems, and Signal Processing
publication date: 1996-03-07T00:00:00Z
review text: An optimal signal reconstruction problem is formulated and treated using multirate space state modeling. The proposed multirate Kalman reconstruction filters noisy, decimated data while producing a good estimate of the complete, clean source. Taking into consideration the analysis of optimal state estimation theory, the multirate Kalman reconstruction filter has achieved the minimum variance estimation and interpolation of noisy and missing ARMA signals. From the investigation of some numerical examples, the multirate Kalman reconstruction filter possesses good estimation/interpolation performances and illustrates significant superiority over a linear interpolator.
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 851947
zbMATH Keywords: multirate space state modeling
zbMATH Keywords: multirate Kalman reconstruction filters
reviewed by: Ioan Dumitrache
MaRDI profile type: Publication
cites work: Kalman filtering with real-time applications
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description: scientific article; zbMATH DE number 851947