Detecting deterministic signals in exceptionally noisy environments using cross-recurrence quantification

The demonstrated ability of recurrence quantification analysis to detect very subtle patterns in time series was exploited to devise a filter able to recognize and extract signals buried in large amounts of noise. The proposed technique, cross-recurrence quantification, demonstrates the ability to extract signals up to a very low signal-to-noise-ratio and to allow an immediate appreciation of their degree of periodicity. The lack of any stationarity dependence of the proposed method opens the way to many possible applications, including encryption. (C) 1998 Elsevier Science B.V.