Title :
A Globally Convergent Matricial Algorithm for Multivariate Spectral Estimation
Author :
Ramponi, Federico ; Ferrante, Augusto ; Pavon, Michele
Author_Institution :
Autom. Control Lab., ETH Zurich, Zurich, Switzerland
Abstract :
In this paper, we first describe a matricial Newton-type algorithm designed to solve the multivariable spectrum approximation problem. We then prove its global convergence. Finally, we apply this approximation procedure to multivariate spectral estimation, and test its effectiveness through simulation. Simulation shows that, in the case of short observation records, this method may provide a valid alternative to standard multivariable identification techniques such as Matlab´s PEM and Matlab´s N4SID.
Keywords :
approximation theory; autoregressive moving average processes; convergence of numerical methods; mathematics computing; convex optimization; globally convergent matricial algorithm; matricial Newton-type algorithm; multivariable identification techniques; multivariable spectrum approximation problem; multivariate spectral estimation; Algorithm design and analysis; Approximation algorithms; Automatic control; Computer languages; Convergence; Entropy; Filters; Frequency; Interpolation; Testing; Convex optimization; Hellinger distance; global convergence; matricial Newton algorithm; multivariable spectrum approximation; spectral estimation;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2009.2028977
