A sinusoidal-signal-assisted method of improving multivariate empirical mode decomposition

As the multivariate extension of empirical mode decomposition, multivariate empirical mode decomposition still suffers the problem of mode mixing. A noise-assisted method has been proposed to reduce mode mixing in multivariate empirical mode decomposition by using the dyadic filter bank property of multivariate empirical mode decomposition when applied to white Gaussian noise. However, the noise-assisted method generates redundant components that do not exist in original signals because the added noise occupies a broad range in the frequency spectrum. We propose a method of using sinusoidal signals, occupying the same frequency spectrum as the original signal, instead of white Gaussian noise to solve this problem. Results show that the new method not only solves the problem of redundant components successfully but also obtains purer modes.