Recent advances in non-stationary signal processing based on the concept of recurrence plot analysis

Abstract

This work concerns the analysis of non-stationary signals using Recurrence Plot Analysis concept. Non-stationary signals are present in real-life phenomena such as underwater mammal’s vocalizations, human speech, ultrasonic monitoring, detection of electrical discharges, transients, wireless communications, etc. This is why a large number of approaches for non-stationary signal analysis are developed such as wavelet analysis, higher order statistics, or quadratic timefrequency analysis. Following the context, the methods defined around the concept of Recurrence Plot Analysis (RPA) constitute an interesting way of analyzing nonstationary signals and, particularly, the transient ones. Starting from the phase space and the recurrence matrix, new approaches [the angular distance, recurrence-based autocorrelation function (ACF), average-magnitude difference function (AMDF)and time-distributed recurrence (TDR)] are introduced in order to extract information about the non-stationary signals, specific to different applications. Comparisons with existing analysis methods are presented, proving the interest and the potential of the RPA-based approaches.