A noise-compensated estimation scheme for AR processes

This paper deals with the problem of identifying autoregressive models in presence of additive measurement noise. A new approach, based on some theoretical results concerning the so-called dynamic Frisch scheme, is proposed. This method takes advantage of both low and high order Yule-Walker equation...

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Bibliographic Details
Published inProceedings of the 44th IEEE Conference on Decision and Control pp. 4146 - 4151
Main Authors Diversi, R., Guidorzi, R., Soverini, U.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Additive noise
Autocorrelation
Equations
Estimation error
Geoscience
Noise measurement
Parameter estimation
Predictive models
Signal processing
Speech analysis
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ISBN9780780395671
0780395670
ISSN0191-2216
DOI10.1109/CDC.2005.1582811

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Summary:This paper deals with the problem of identifying autoregressive models in presence of additive measurement noise. A new approach, based on some theoretical results concerning the so-called dynamic Frisch scheme, is proposed. This method takes advantage of both low and high order Yule-Walker equations and allows to identify the AR parameters and the driving and output noise variances in a congruent way since the estimates assure the positive definiteness of the autocorrelation matrix of the AR process. Simulation results are reported to show the effectiveness of the proposed procedure and compare its performance with those of other identification methods.
ISBN:9780780395671
0780395670
ISSN:0191-2216
DOI:10.1109/CDC.2005.1582811