Fanizza, Giovanna (Royal Inst. of Tech.), Nagamune, Ryozo (Royal Inst. of Tech.)

Spectral Estimation by Numerical Optimization Based on Rational Covariance Extension

Scheduled for presentation during the Regular Session "Linear System Identification III" (WeA09), Wednesday, July 26, 2006, 11:40−12:05, Room J

17th International Symposium on Mathematical Theory of Networks and Systems, July 24-28, 2006, Kyoto, Japan

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

This paper proposes a new spectral estimation technique based on rational covariance extension with degree constraint. The technique finds a rational spectral density function that approximates given spectral density data under constraint on a covariance sequence. A spectral density approximation problem is formulated as nonconvex optimization problem with respect to a Schur polynomial. To formulate the approximation problem, the least-squares sum is considered as a distance. Properties of the optimization problem and a numerical algorithm to solve it is explained. A numerical example illustrate how the method discussed in this paper are useful in stochastic process modeling.