Open AccessA fast robust numerical continuation solver to a two‐dimensional spectral estimation problem
Author(s) -
Zhu Bin,
Liu Jiahao
Publication year - 2022
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12276
Subject(s) - solver , hessian matrix , mathematics , estimator , continuation , moment (physics) , mathematical optimization , newton's method , algorithm , computer science , statistics , physics , classical mechanics , nonlinear system , quantum mechanics , programming language
This paper presents a fast algorithm to solve a spectral estimation problem for two‐dimensional random fields. The latter is formulated as a convex optimization problem with the Itakura‐Saito pseudodistance as the objective function subject to the constraints of moment equations. The structure of the Hessian of the dual objective function is exploited in order to make possible a fast Newton solver. Then the Newton solver is incorporated to a predictor‐corrector numerical continuation method which is able to produce a parametrized family of solutions to the moment equations. Two sets of numerical simulations are performed to test the algorithm and spectral estimator. The simulations on the frequency estimation problem show that our spectral estimator outperforms the classical windowed periodograms in the case of two hidden frequencies and has a higher resolution. The other set of simulations on system identification indicates that the numerical continuation method is more robust than Newton's method alone in ill‐conditioned instances.