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Conference Paper: Multirate Kalman filtering approach for optimal two-dimensional signal reconstruction from noisy subband systems

TitleMultirate Kalman filtering approach for optimal two-dimensional signal reconstruction from noisy subband systems
Authors
Issue Date1997
PublisherIEEE.
Citation
Ieee International Conference On Image Processing, 1997, v. 1, p. 157-160 How to Cite?
AbstractConventional synthesis filters in subband systems lose their optimality when additive noise due, for example, to signal quantization, disturbs the subband components. The multichannel representation of subband signal is combined with the statistical model of input signal to derive the multirate state-space model for filter bank system with additive noises. Thus the signal reconstruction problem in subband system can be formulated as the process of optimal state estimation in the equivalent multirate state-space model. With the input signal embedded in the state vector, the multirate Kalman filtering provides the minimum-variance reconstruction of input signal. Using the powerful Kronecker product notation, the results and derivations can then be extended to the 2-D cases. Incorporated with the vector dynamical model, the 2-D multirate state-space model for 2-D Kalman filtering is developed. Computer simulation with the proposed 2-D multirate Kalman filter gives favorable results.
DescriptionThe International Conference on Image Processing, Santa Barbara, California, 26-29 October 1997
Persistent Identifierhttp://hdl.handle.net/10722/54045
ISSN

 

DC FieldValueLanguage
dc.contributor.authorNi, JQen_HK
dc.contributor.authorHo, KLen_HK
dc.contributor.authorTse, KWen_HK
dc.contributor.authorNi, JSen_HK
dc.contributor.authorShen, MHen_HK
dc.date.accessioned2009-04-03T07:35:20Z-
dc.date.available2009-04-03T07:35:20Z-
dc.date.issued1997en_HK
dc.identifier.citationIeee International Conference On Image Processing, 1997, v. 1, p. 157-160en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/54045-
dc.descriptionThe International Conference on Image Processing, Santa Barbara, California, 26-29 October 1997-
dc.description.abstractConventional synthesis filters in subband systems lose their optimality when additive noise due, for example, to signal quantization, disturbs the subband components. The multichannel representation of subband signal is combined with the statistical model of input signal to derive the multirate state-space model for filter bank system with additive noises. Thus the signal reconstruction problem in subband system can be formulated as the process of optimal state estimation in the equivalent multirate state-space model. With the input signal embedded in the state vector, the multirate Kalman filtering provides the minimum-variance reconstruction of input signal. Using the powerful Kronecker product notation, the results and derivations can then be extended to the 2-D cases. Incorporated with the vector dynamical model, the 2-D multirate state-space model for 2-D Kalman filtering is developed. Computer simulation with the proposed 2-D multirate Kalman filter gives favorable results.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE International Conference on Image Processingen_HK
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleMultirate Kalman filtering approach for optimal two-dimensional signal reconstruction from noisy subband systemsen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1522-4880&volume=&spage=159&epage=160&date=1997&atitle=Multirate+Kalman+filtering+approach+for+optimal+two-dimensional+signal+reconstruction+from+noisy+subband+systemsen_HK
dc.identifier.emailHo, KL: klho@eee.hku.hken_HK
dc.identifier.emailTse, KW: kwtse@eee.hku.hken_HK
dc.identifier.authorityHo, KL=rp00117en_HK
dc.identifier.authorityTse, KW=rp00180en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.scopuseid_2-s2.0-0031342414en_HK
dc.identifier.hkuros33216-
dc.identifier.volume1en_HK
dc.identifier.spage157en_HK
dc.identifier.epage160en_HK
dc.identifier.scopusauthoridNi, JQ=7201636714en_HK
dc.identifier.scopusauthoridHo, KL=7403581592en_HK
dc.identifier.scopusauthoridTse, KW=7102609851en_HK
dc.identifier.scopusauthoridNi, JS=35771345200en_HK
dc.identifier.scopusauthoridShen, MH=7401466082en_HK

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