Dynamically-Sampled Bivariate Empirical Mode Decomposition

A novel scheme for selecting projection vectors in bivariate empirical mode decomposition (BEMD) is proposed in order to enable accurate signal decomposition at lower computational complexity. Unlike existing algorithms which use a static uniform scheme for the distribution of projection vectors, the proposed scheme examines local curvature in multidimensional spaces to produce a data-adaptive set of direction vectors for taking signal projections. This is achieved by aligning the density of projection vectors according to the empirical distributions of angles where the signal exhibits highest local dynamics. We show that the proposed methodology outperforms the existing schemes for a small number of signal projections. The proposed algorithm is verified via illustrative simulations demonstrating accurate local mean estimation and mode extraction.