Method and device thereof for performing hierarchical empirical mode decomposition on signal

Abstract

The invention discloses a method and a device thereof for performing hierarchical empirical mode decomposition on a signal. In the embodiment of the invention, when the empirical mode decomposition is performed on the input signal, an artificial auxiliary signal is combined with the input signal in aid of searching an extreme value; and frequency reduction is performed in each iterative process so as to eliminate the artificial auxiliary signal and perform mode convergence at the same time to prevent mode mixing. Moreover, one embodiment of the invention provides a hierarchical decomposition method, which comprises the following steps: first, decomposing the signal into a relatively smaller number of intrinsic modes; and then expanding one intrinsic mode into expansion modes according to needs in application. In the embodiment, the mode decomposed by the hierarchical empirical mode decomposition under the proper frequency reduction is substantially irrelative to an envelope construction method; and the hierarchical empirical mode decomposition can be applied to the decomposition of a multi-dimensional signal.

Description

technical field [0001] The invention relates to a signal processing method, and in particular to a signal processing method of empirical mode decomposition. Background technique [0002] Empirical Mode Decomposition (EMD) method was proposed by Huang N.E. et al. to decompose non-stationary and non-linear signals. The signal decomposition algorithm can decompose a time-related signal into a superposition of finite intrinsic mode functions (IMFs) and signal residual functions (monotonic functions). [0003] In the past few years, many scholars have developed the empirical mode decomposition method to two dimensions. According to the method, it can be divided into the following two categories, one-way empirical mode decomposition method (Single direction EMD methods) (Patent No. US6311130) and two-dimensional interpolation Function-based empirical mode decomposition method (Bi-dimensionalinterpolation function based EMD methods). The former treats the image as a one-dimension...

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Problems solved by technology

At present, Huang E proposes to adopt the cubic curve as the best empirical solution for one-dimensional decomposition, but there is no optimal empirical solution for the decomposition of more than two dimensions. Therefore, a variety of envelope methods have been developed accordingly. However, these envelope methods also have different models. state decomposition result

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