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传统经验模态分解(EMD)去噪方法和改进的去噪方法都是在EMD分解基础之上进行的,没有对信号在频带上进行更充分的分解与去噪。为了获得更高的信噪比,首先对带噪信号进行多小波包分解。对小波系数进行阈值去噪后,在小波框架下将EMD区间迭代不变阈值分解(EMD-CIIT)去噪方法直接作用于小波系数上。最后重构经过EMD-CIIT处理后的小波系数。通过仿真验证了算法的可行性与有效性。结果表明小波包与多小波包框架下的EMD-CIIT去噪效果优于传统EMD-CIIT去噪方法。在多数情况下小波包框架下的EMD-CIIT去噪算法优于多小波包框架下的EMD-CIIT去噪算法。
The traditional empirical mode decomposition (EMD) denoising method and the improved denoising method are all based on the EMD decomposition without any more decomposition and denoising of the signal in the frequency band. In order to obtain a higher signal-to-noise ratio, multi-wavelet packet decomposition of the noisy signal is first performed. After denoising the wavelet coefficients, the EMD interval iterative invariant threshold decomposition (EMD-CIIT) denoising method is directly applied to the wavelet coefficients under the wavelet framework. Finally, the wavelet coefficients after EMD-CIIT processing are reconstructed. Simulation shows the feasibility and effectiveness of the algorithm. The results show that the EMD-CIIT denoising effect under wavelet packet and multi-wavelet packet framework is better than the traditional EMD-CIIT denoising method. In most cases, the EMD-CIIT denoising algorithm under the wavelet packet framework is superior to the EMD-CIIT denoising algorithm under the multi-wavelet packet framework.