A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TV AR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (lLS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.

Iterative approach to estimate the parameters of a TVAR process corrupted by a MA noise

DIVERSI, ROBERTO;
2014

Abstract

A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TV AR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (lLS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.
2014
Proceedings of the 22nd European Signal Processing Conference
456
460
Hiroshi Ijima; Roberto Diversi; Eric Grivel
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/464367
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