Singular Spectrum Analysis (SSA) is a signal decomposition technique that aims at expanding signals into interpretable and physically meaningful components (e.g. sinusoids, noise, etc.). This article presents new theoretical and practical results about the separability of the SSA and introduces a new method called sliding SSA. First, the SSA is combined with an unsupervised classification algorithm to provide a fully automatic data-driven component extraction method for which we investigate the limitations for components separation in a theoretical study. Second, the detailed automatic SSA method is used to design an approach based on a sliding analysis window which provides better results than the classical SSA method when analyzing non-stationary signals with a time-varying number of components. Finally, the proposed sliding SSA method is compared to the Empirical Mode Decomposition (EMD) and to the synchrosqueezed Short-Time Fourier Transform (STFT), applied on both synthetic and real-world signals.

International audienceThis letter introduces new chirp rate and instantaneous frequency estimators designed for frequency modulated signals. These estimators are first investigated from a deterministic point of view, then compared together in terms of statistical efficiency. They are also used to design new recursive versions of the vertically synchrosqueezed short-time Fourier transform, using a previously published method. This study paves the way to the real-time computation of a time-frequency representation which is both invertible and sharply localized in frequency

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Abstract-In this paper, we introduce the ASTRES * toolbox which offers a set of Matlab functions for non-stationary multicomponent signal processing. The main purposes of this proposal is to offer efficient tools for analysis, synthesis and transformation of any signal made of physically meaningful components (e.g. sinusoid, trend or noise). The proposed techniques contain some recent and new contributions, which are now unified and theoretically strengthened. They can provide efficient time-frequency or time-scale representations and they allow elementary components extraction. Usage and description of each method are then detailed and numerically illustrated.

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