SIGNAL CLASSIFICATION BY POWER SPECTRAL DENSITY: AN APPROACH VIA RIEMANNIAN GEOMETRY

The power spectral density (PSD) of a signal is often used as a feature for signal classification for which a distance measure must be chosen to compare the similarity between the signal features. We reason that PSD matrices have structural constraints and describe a manifold in the signal space. Thus, instead of the widely used Euclidean distance (ED), a more appropriate measure is the Riemannian distance (RD) on the manifold. Here, we develop …