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The Rational Covariance Extension Problem is a problemin applied mathematics where one tries to find a rational spectral density thatmatches a finite covariance sequence. Applications of this can be used in areaslike speech- and image-processing. This problem has been studied intensivelyover the last decades and recently a related problem, the Circulant RationalCovariance Extension Problem, was solved. This version of the problem dealswith periodic stochastic sequences, and was shown to be a natural way toapproximate the solution to the original problem. Here we look at the specialcase when the process in question is skew-periodic, and show that also in thiscase a unique solution to the problem exists. Moreover we develop numerical solversfor both the periodic and the skew-periodic problem, and use these algorithms toapproximate the spectrum from a speech signal. / Det Rationella Kovariansutvidgningsproblemet är ett problem inom tillämpad matematik där man försöker hitta en rationell spektraltäthet som matchar en given sekvens av kovarianser. Tillämpningar av problemet finns inom områden som tal- och bildbehandling. Problemet har studerats intensivt under de senaste decennierna, och nyligen har ett relaterat problem lösts - nämligen det Cirkulära Rationella Kovariansutvidgningsproblemet. I detta problem arbetar man med periodiska stokastiska processer, och lösningen visade sig vara ett naturligt sätt att approximera lösningen till det första problemet. I denna uppsats tittar vi på specialfallet när processen är skev-periodisk, och visar att det även i detta fall finns en unik lösning. Dessutom utvecklas numeriska lösare för både det periodiska och skev-periodiska problemet, och dessa algoritmer används tillslut för att approximera spektrumet för en talsignal.

This thesis consists of six papers. The main topic of all these papers is modeling a class of linear time-invariant systems. The system class is parameterized in the context of interpolation theory with a degree constraint. In the papers included in the thesis, this parameterization is the key tool for the design of dynamical system models in fields such as spectral estimation and model reduction. A problem in spectral estimation amounts to estimating a spectral density function that captures characteristics of the stochastic process, such as covariance, cepstrum, Markov parameters and the frequency response of the process. A  model reduction problem consists in finding a small order system which replaces the original one so that the behavior of both systems is similar in an appropriately defined sense.  In Paper A a new spectral estimation technique based on the rational covariance extension theory is proposed. The novelty of this approach is in the design of a spectral density that optimally matches covariances and approximates the frequency response of a given process simultaneously.In Paper B  a model reduction problem is considered. In the literature there are several methods to perform model reduction. Our attention is focused on methods which preserve, in the model reduction phase, the stability and the positive real properties of the original system. A reduced-order model is computed employing the analytic interpolation theory with a degree constraint. We observe that in this theory there is a freedom in the placement of the spectral zeros and interpolation points. This freedom can be utilized for the computation of a rational positive real function of low degree which approximates the best a given system. A problem left open in Paper B is how to select spectral zeros and interpolation points in a systematic way in order to obtain the best approximation of a given system. This problem is the main topic in Paper C. Here, the problem is investigated in the analytic interpolation context and spectral zeros and interpolation points are obtained as solution of a optimization problem.In Paper D, the problem of modeling a floating body by a positive real function is investigated. The main focus is  on modeling the radiation forces and moment. The radiation forces are described as the forces that make a floating body oscillate in calm water. These forces are passive and usually they are modeled with system of high degree. Thus, for efficient computer simulation it is necessary to obtain a low order system which approximates the original one. In this paper, the procedure developed in Paper C is employed. Thus, this paper demonstrates the usefulness of the methodology described in Paper C for a real world application.In Paper E, an algorithm to compute the steady-state solution of a discrete-type Riccati equation, the Covariance Extension Equation, is considered. The algorithm is based on a homotopy continuation method with predictor-corrector steps. Although this approach does not seem to offer particular advantage to previous solvers, it provides insights into issues such as positive degree and model reduction, since the rank of the solution of the covariance extension problem coincides with the degree of the shaping filter. In Paper F a new algorithm for the computation of the analytic interpolant of a bounded degree is proposed. It applies to the class of non-strictly positive real interpolants and it is capable of treating the case with boundary spectral zeros. Thus, in Paper~F, we deal with a class of interpolation problems which could not be treated by the optimization-based algorithm proposed by Byrnes, Georgiou and Lindquist. The new procedure computes interpolants by solving a system of nonlinear equations. The solution of the system of nonlinear equations is obtained by a homotopy continuation method. / QC 20100721