Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations
Date
2011-05
Authors
Jamoos, Ali
Grivel, Eric
Shakarneh, Nidal
Abdel-Nour, Hanna
Journal Title
Journal ISSN
Volume Title
Publisher
IET Signal Processing
Abstract
This study deals with the estimation of a vector process disturbed by an additive white noise. When this process is
modelled by a multivariate autoregressive (M-AR) process, optimal filters such as Kalman or H1 filter can be used for
prediction or estimation from noisy observations. However, the estimation of the M-AR parameters from noisy observations is
a key issue to be addressed. Off-line or iterative approaches have been proposed recently, but their computational costs can be
a drawback. Using on-line methods such as extended Kalman filter and sigma-point Kalman filter are of interest, but the size
of the state vector to be estimated is quite high. In order to reduce this size and the resulting computational cost, the authors
suggest using dual optimal filters. In this study, the authors propose to extend to the multi-channel case the so-called dual
Kalman or H1 filters-based scheme initially proposed for single-channel applications. The proposed methods are first tested
with a synthetic M-AR process and then with an M-AR process corresponding to a mobile fading channel. The comparative
simulation study the authors carried out with existing techniques confirms the effectiveness of the proposed methods.
Description
Keywords
optimal filters , parameter estimation , multivariate autoregressive process , fading channels