Polarized signal singular spectrum analysis with complex SSA

This paper considers the analysis of bivariate signals using complex Singular Spectrum Analysis (SSA). It introduces a pseudo-correlation based criterion in the grouping step of complex SSA. The advantage of using pseudo-correlation rather than correlation measures when analyzing polarized signals with complex SSA is demonstrated theoretically. This criterion is shown to be effective to extract bivariate signals modeled as particular complex Linear Recurrence Relations (LRR) of order 2. These elementary complex bricks offer a high interpretability in terms of polarization. Illustration of the proposed grouping technique is made through polarized component extraction on a real-world data example.