Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Automatica (Journal of IFAC)
A subspace algorithm for the identification of discrete time frequency domain power spectra
Automatica (Journal of IFAC)
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Vector ARMA estimation: a reliable subspace approach
IEEE Transactions on Signal Processing
Subspace-based rational interpolation of analytic functions from phase data
IEEE Transactions on Signal Processing
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This paper presents a subspace-based identification algorithm for estimating the state-space quadruple [A,B,C,D] of a minimum-phase spectral factor from matrix-valued power spectrum data. For a given pair [A,C] with A stable, the minimum-phase property is guaranteed via the solution of a conic linear programming (CLP) problem. In comparison with the classical LMI-based solution, this results in a more efficient way to minimize the weighted 2-norm of the error between the estimated and given power spectrum. The conic linear programming problem can be solved in a globally optimal sense. This property is exploited in the derivation of a separable least-squares procedure for the (local) minimization of the above 2-norm with respect to the parameters of the minimal phase spectral factor. The advantages of the derived subspace algorithm and the iterative local minimization procedure are illustrated in a brief simulation study. In this study, the effect of dealing with short length data sets for computing the power spectrum, on the estimated spectral factor, is illustrated.