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.

A noise-compensated estimation scheme for AR processes

DIVERSI, ROBERTO;GUIDORZI, ROBERTO;SOVERINI, UMBERTO
2005

Abstract

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.
Proceedings of 44th IEEE Conference on Decision and Control and European Control Conference ECC'05
4146
4151
R. Diversi; R. Guidorzi; U. Soverini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/22321
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