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Identification of autoregressive models in the presence of additive noise
- Source :
- International Journal of Adaptive Control and Signal Processing. 22:465-481
- Publication Year :
- 2008
- Publisher :
- Wiley, 2008.
-
Abstract
- A common approach in modeling signals in many engineering applications consists in adopting autoregressive (AR) models, consisting in filters with transfer functions having a unitary numerator, driven by white noise. Despite their wide application, these models do not take into account the possible presence of errors on the observations and cannot prove accurate when these errors are significant. AR plus noise models constitute an extension of AR models that consider also the presence of an observation noise. This paper describes a new algorithm for the identification of AR plus noise models that is characterized by a very good compromise between accuracy and efficiency. This algorithm, taking advantage of both low and high-order Yule–Walker equations, also guarantees the positive definiteness of the autocorrelation matrix of the estimated process and allows to estimate the equation error and observation noise variances. It is also shown how the proposed procedure can be used for estimating the order of the AR model. The new algorithm is compared with some traditional algorithms by means of Monte Carlo simulations. Copyright © 2007 John Wiley & Sons, Ltd.
- Subjects :
- SYSTEM IDENTIFICATION
Speech recognition
Monte Carlo method
YULE–WALKER EQUATIONS
System identification
White noise
Noise
Autoregressive model
Positive definiteness
Control and Systems Engineering
Autocorrelation matrix
Signal Processing
NOISY AUTOREGRESSIVE MODELS
Electrical and Electronic Engineering
Algorithm
STAR model
Mathematics
Subjects
Details
- ISSN :
- 10991115 and 08906327
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- International Journal of Adaptive Control and Signal Processing
- Accession number :
- edsair.doi.dedup.....860f291fb53d708c70b8fdde93031bf9