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Signal De-noising method based on particle swarm algorithm and Wavelet transform

机译：基于粒子群算法和小波变换的信号降噪方法

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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.
机译：小波分析是在傅立叶分析的基础上开发的一种新型的时频分析工具，具有良好的时频定位特性和多分辨率特性，在信号处理领域有着广泛的应用。本文研究了小波变换在信号滤波中的应用，通过改进的粒子群算法，提出了一种基于小波分析的智能信号降噪方法。该方法使用基于中心的粒子群算法（CBPSO）为不同比例的每个子带选择最佳阈值，从信号本身智能地学习噪声类型，而无需任何先验噪声知识。改进的粒子群算法用于增强小波域阈值不同尺度的最优选择，实现了在不同类型噪声背景下的信号去噪，提高了小波变换和小波构造的速度，具有更大的灵活性。实验结果表明，CBPSO算法具有较好的去噪效果。

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