Advanced Processing of Nonuniformly Sampled Non-Stationary Signals

Abstract

A signal is stationary if its statistical characteristics do not change with time. Signals of practical interest often do not comply with this requirement. Short time Fourier transform, time-frequency distribution and wavelet transform are the classical approaches used to analyze nonstationary signals. However they have limited applicability if the signal sampling density is below Nyquist. Time-frequency analysis typically deals with signals, where the instantaneous bandwidth is considerably narrower than the bandwidth of analysis. The paper proposes an enhancement of non-stationary signal processing, which is based on the adaptation of transformation functions to instantaneous spectrum. The main advantages of the proposed approach are increased resolution, suppressed side-loops and cross-terms and applicability to nonuniform sampling with a sampling density less than the Nyquist rate. Ill. 4, bibl. 11 (in English, summaries in Lithuanian, English, Russian).