Repository logo
 

Two-dimensional recursive parameter identification for adaptive Kalman filtering

Simple item page
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorBannour, Sami, author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:18:37Z
dc.date.available2007-01-03T04:18:37Z
dc.date.issued1991
dc.description.abstractThis paper is concerned with the development of a 2-D adaptive Kalman filtering by recursive adjustment of the parameters of an autoregressive (AR) image model with non symmetric half-plane (NSHP) region of support. The image and degradation models are formulated in a 2-D state-space model, for which the relevant 2-D Kalman filtering equations are given. The recursive parameter identification is achieved using the extension of the stochastic Newton approach to the 2-D case. This process can be implemented on-line to estimate the image model parameters based upon the local statistics in every processing window. Simulation results for removing an additive noise from a degraded image are also presented.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationAzimi-Sadjadi, Mahmood R. and Sami Bannour, Two-Dimensional Recursive Parameter Identification for Adaptive Kalman Filtering, IEEE Transactions on Circuits and Systems 38, no. 9 (September 1991): 1077-1081.
dc.identifier.urihttp://hdl.handle.net/10217/1018
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1991 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjecttwo-dimensional digital filters
dc.subjectadaptive filters
dc.subjectparameter estimation
dc.subjectpicture processing
dc.subjectstate-space methods
dc.subjectKalman filters
dc.subjectfiltering and prediction theory
dc.titleTwo-dimensional recursive parameter identification for adaptive Kalman filtering
dc.typeText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ECEmra00029.pdf
Size:
490.74 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Faculty Publications