Technical gazette, Vol. 21 No. 5, 2014.
Original scientific paper
Signal De-noising method based on particle swarm algorithm and Wavelet transform
Zhuang Wu
; Information College, Capital University of Economics and Business, 121 Zhangjialukou, Huaxiang Fengtai District, Beijing 100070, China
Abstract
Wavelet analysis is a new time-frequency analysis tool developed on the basis of Fourier analysis with good time-frequency localization property and multi-resolution characteristics, which is in a wide range of applications in the field of signal processing. This paper studies the application of wavelet transform in signal filtering, by using an improved particle swarm optimization, proposes an intelligent signal de-noising method based on wavelet analysis. The method uses a Center Based Particle Swarm Algorithm (CBPSO) to select the optimal threshold for each sub-band in different scales, learning the type of noise from the signal itself intelligently, which does not require any prior knowledge of the noise. The improved particle swarm algorithm is used to enhance the optimal choice of the different scales of the wavelet domain threshold, which realized the signal De-noising under different types of noise background, and improved the speed of wavelet transform and wavelet construction, and has greater flexibility. The experimental results showed that CBPSO algorithm can get better De-noising effect.
Keywords
wavelet analysis; particle swarm algorithm; signal De-noising
Hrčak ID:
129047
URI
Publication date:
29.10.2014.
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