Abstract
In this paper, the properties of two recently proposed frequency-domain subspace-based algorithms to estimate discrete-time cross-power spectral density (cross-PSD) and auto-power spectral density (auto-PSD) matrices of vector auto-regressive moving-average and moving-average (ARMAMA) models from sampled values of the Welch cross-PSD and auto-PSD estimators on uniform grids of frequencies, are illustrated by numerical and real-life application examples. The latter is concerned with the modeling of acoustic spectra for detecting faults in induction motors.
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