A noise-compensated estimation scheme for AR processes

• Published in: Proceedings of the 44th IEEE Conference on Decision and Control pp. 4146 - 4151
• Main Authors: Diversi, R., Guidorzi, R., Soverini, U.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Additive noise; Autocorrelation; Equations; Estimation error; Geoscience; Noise measurement; Parameter estimation; Predictive models; Signal processing; Speech analysis
• ISBN: 9780780395671; 0780395670
• ISSN: 0191-2216
• DOI: 10.1109/CDC.2005.1582811

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.