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Titolo:
Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: theory and application
Autore:
Zheng, YJ; Tay, DBH; Lin, ZP;
Indirizzi:
Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore Nanyang Technol Univ Singapore Singapore 639798 gapore 639798, Singapore
Titolo Testata:
SIGNAL PROCESSING
fascicolo: 9, volume: 81, anno: 2001,
pagine: 1823 - 1848
SICI:
0165-1684(200109)81:9<1823:MGDNPA>2.0.ZU;2-F
Fonte:
ISI
Lingua:
ENG
Soggetto:
BLIND EQUALIZATION; SUBSPACE METHODS; LEAST-SQUARES; IDENTIFICATION; ALGORITHM; SIGNALS; CHANNELS; SPEECH;
Keywords:
discrete orthogonal wavelet transform; Gaussian time-varying AR model; non-Gaussian time-varying AR model; system identification; fast fading channel; blind equalization;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
55
Recensione:
Indirizzi per estratti:
Indirizzo: Lin, ZP Nanyang Technol Univ, Sch Elect & Elect Engn, Nanyang Ave, Singapore 639798, Singapore Nanyang Technol Univ Nanyang Ave Singapore Singapore 639798 apore
Citazione:
Y.J. Zheng et al., "Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: theory and application", SIGNAL PROC, 81(9), 2001, pp. 1823-1848

Abstract

In this paper, some new techniques for time-varying parametric autoregressive (AR) system identification by wavelets are presented. Firstly, we derive a new multiresolution least squares (MLS) algorithm for Gaussian time-varying AR model identification employing wavelet operator matrix representation. This method can optimally balance between the over-fitted solution and the poorly represented identification. The main features of the time-varying model parameters are estimated by a multiresoulution method, which represents the smooth trends as well as the rapidly changing components. Combining the total least squares algorithm with the MLS algorithm, a new method ispresented which can make the identification of a noisy time-varying AR model. Finally, we deal with a non-Gaussian time-varying AR model for modelingnonstationary processes in a non-Gaussian distribution. A pseudo-maximum likelihood estimation algorithm is proposed for this model identification, The time-varying AR parameters as well as the non-Gaussian probability density (approximated by Gaussian mixture density) parameters of the driving noise sequence (DNS) are simultaneously estimated. Simulation results verify that our methods can effectively identify time-varying AR systems with general distributed DNS. A realistic application of the proposed technique in blind equalization of time-varying fading channel will be explored. (C) 2001 Elsevier Science B.N. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 10/07/20 alle ore 08:20:34