Empirical mode decomposition (EMD) is one of effective methods to process nonlinear and non-stationary signals, its key is to extract signals’ envelope curve. A method based on the sparse recovery optimizing algorithm was proposed to overcome defects of the envelope fitting algorithm, such as, end effect, bigger fitting error and low anti-noise ability, etc. Firstly, the concave problem of envelope sparse optimal model was converted into a convex quadratic programming problem by using exterior penalty functions. Secondly, the mixed variant particle swarm optimization (PSO) algorithm was used to solve the global optimization of the variant factor m which changes the sparse base’s frequency band width. This m was employed to build the optimal sparse bases being suitable for envelope variation trend. All extreme value points of the collected signal were taken as observed values in the process of sparse recovery. The optimal sparse bases and observed values were used to establish the sparse recovery model. The interior-point method was adopted to process the built model. Finally, the globally optimal envelope signal was gained adaptively. The results showed that this method can effectively suppress the end effect; PSO introduced here can adaptively match the mapping band width of the optimal spare bases, it improves the signal envelope fitting precision and noise immunity.
Key words
empirical mode decomposition (EMD) /
sparse recovery optimization /
particle swarm optimization (PSO) /
interior-point method
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