Evolutive method based on a generalized eigenvalue decomposition to estimate time varying autoregressive parameters from noisy o

A great deal of interest has been paid to the estimation of time-varying autoregressive (TVAR) parameters. However, when the observations are disturbed by an additive white measurement noise, using standard least squares methods leads to a weight-estimation bias. In this paper, we propose to jointly estimate the TVAR parameters and the measurement-noise variance from noisy observations by means of a generalized eigenvalue decomposition. It extends to the TVAR case an off-line method that was initially proposed for AR parameter estimation from noisy observations. A comparative study is then carried out with existing methods such as the recursive errors-in-variable approach and Kalman based algorithms.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Hiroshi Ijima, Julien Petitjean, Eric Grivel
• Keywords: Additive White Measurement, ICASSP 2011, Noisy Observations, Parameter, Signal Processing