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EnglishThis paper considers the problem of estimating the parameters of an autoregressive (AR) process in presence of additive white noise and proposes a new identification method, based on theoretical results originally developed in errors-in-variables contexts. This approach allows to estimate the AR parameters, the driving noise variance and the variance of the additive noise in a congruent way in that these estimates assure the positive definiteness of the autocorrelation matrix. The performance of the proposed algorithm is compared with that of bias-compensated least-squares methods by means fo Monte Carlo simulations. The results show the effectivenesss of the new method also in presence of high amounts of noise.
| Titolo: | A new estimation approach for AR models in presence of noise | |
| Autore/i: | DIVERSI, ROBERTO; SOVERINI, UMBERTO; GUIDORZI, ROBERTO | |
| Autore/i Unibo: | ||
| Anno: | 2005 | |
| Titolo del libro: | Preprints of the 16th IFAC World Congress | |
| Abstract: | This paper considers the problem of estimating the parameters of an autoregressive (AR) process in presence of additive white noise and proposes a new identification method, based on theoretical results originally developed in errors-in-variables contexts. This approach allows to estimate the AR parameters, the driving noise variance and the variance of the additive noise in a congruent way in that these estimates assure the positive definiteness of the autocorrelation matrix. The performance of the proposed algorithm is compared with that of bias-compensated least-squares methods by means fo Monte Carlo simulations. The results show the effectivenesss of the new method also in presence of high amounts of noise. | |
| Data prodotto definitivo in UGOV: | 17-ott-2005 | |
| Appare nelle tipologie: | 4.01 Contributo in Atti di convegno |
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