Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

• DOI: 10.3390/s20123373
• Publication Date: 2020-06-15T16:16:57Z
• Authors: Kai Chen; Kai Xie; Chang Wen; Xin-Gong Tang
• Keywords: weak signal; complete ensemble empirical mode; parallel computing; Chemical technology; generative adversarial network; cyclic neural network; 0202 electrical engineering, electronic engineering, information engineering; strong background noises; TP1-1185; 02 engineering and technology; graphical processing unit; Article

In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.