Recursive Separation of Stationary Components by Subspace Projection and Stochastic Constraints

We propose a filtration approach to discriminate between stationary and non-stationary signals which consist into recursively update an enhanced representation of input time-series in such a way that the decomposition is able to identify time-varying statistical parameters of the data. The approach is based on the hypothesis that such updating providing a time-varying subspace projection under stationary constraints, allows to obtain a better separation. Validation of quality separation is carried on simulated and real data. In both cases, obtained separation shows that proposed approach is able to identify different dynamics on analyzed data.