Feature sets for nonstationary signals derived from moments of the singular value decomposition of Cohen-Posch (positive time-frequency) distributions

This correspondence presents a new method for determining the principal features of a nonstationary time series process based on the singular value decomposition (SVD) of the Cohen–Posch positive time–frequency distribution. This new method uses density functions derived from the SVD singular vectors to generate moments that are associated with the principal features of the nonstationary process. Since the SVD singular vectors are orthonormal, the vectors whose elements are composed of the squared elements of the SVD vectors are discrete density functions. Moments generated from these density functions are the principal features of the nonstationary time series process. The main reason for determining features of a time series process is to characterize it by a few simple descriptors.