By Topic

All-Pole Estimation in Spectral Domain

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Luis Weruaga ; Austrian Acad. of Sci., Vienna

Autoregressive (AR) modeling is a popular spectral analysis method commonly resolved in the time domain. This paper presents a novel AR analysis framework dealing with the estimation of poles directly from spectral samples. The basis of the method lies on a minimizing functional built with a certain mapping of the spectral residue. The optimization mechanism is based on the multivariate Newton-Raphson algorithm. Two different mappings are considered, namely, linear and logarithmic. The linear case results in a nonquadratic convex functional, whose global minimum is equivalent to that of the time-domain autocorrelation method. The logarithmic case under the maximum likelihood criterion turns out equivalent to the Whittle likelihood, proven here to be suitable for frequency selective estimation. The statistical and convergence performance of the method is demonstrated with simulations on stochastic and deterministic harmonic signals.

Published in:

IEEE Transactions on Signal Processing  (Volume:55 ,  Issue: 10 )