A circular strategies signal extraction method based on CEEMD and permutation entropy
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摘要: 针对EEMD阈值降噪处理方法时效性差且噪声难以准确估计问题，提出了基于CEEMD-排列熵的循环策略信号提取方法。原始信号经CEEMD处理，对信号叠加相反白噪声抑制白噪声引起重构误差的同时简化了计算方法，对分解得到的本征模态分量通过计算排列熵确定噪声分量和信号分量，考虑到信号中噪声先验知识未知，在奇异值分解的基础上，建立信号提取的循环策略，该方法不需要信号任何先验知识，在去噪同时，还可以减少有用细节失真。通过仿真信号和混沌信号降噪处理，结果表明所提方法不仅有效剔除了噪声干扰，而且减少了有用细节流失。Abstract: In order to solve the problem of the poor timeliness and the difficulty of accurate noise estimation of EEMD threshold de-noising method, a circular strategies signal extraction method based on CEEMD permutation entropy was proposed. The original signal was processed by CEEMD, and the reconstruction error caused by white noise was suppressed by superimposing the opposite white noise on the signal. The noise component and signal component were determined by calculating permutation entropy of the intrinsic mode functions. Considering the unknown prior knowledge of noise of the signal, the signal extraction method of circular strategies was established on the basis of singular value decomposition without prior knowledge of signal and noise, the useful signal could be well preserved while noise was suppressed. Through the noise reduction of simulation signal and chaotic signal, the results showed that the proposed method not only effectively eliminated the noise interference, but also reduced the loss of useful details.
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Key words:
- CEEMD /
- permutation entropy /
- SVD /
- de-noising /
- chaotic
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表 1 几种信号的排列熵
信号类型 白噪声 高斯白噪声 正弦信号 调幅信号 调幅调频信号 间歇性信号 排列熵 0.969 8 0.971 4 0.373 9 0.242 4 0.262 9 0.614 6 
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表 2 4种方法各项指标比较
方法 噪声幅值 噪声次数 计算耗时/s 信噪比/dB RMSE EEMD 0.1 100 28.257 3 5.374 2 0.783 5 传统CEEMD 0.1 50×2 8.375 2 5.536 8 0.733 2 CEEMD-小波 0.1 50×2 8.652 4 6.358 9 0.657 4 本文方法 0.1 50×2 9.473 5 7.277 2 0.522 3
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