Single-channel blind source separation method of spatial aliasing signal based on Stacked-TCN [基于Stacked-TCN的空间混叠信号单通道盲源分离方法]

作者：Zhao M.; Yao X.; Wang J.; Dong S.

来源：Systems Engineering and Electronics, 2021, 43(9): 2628-2636.

DOI：10.12305/j.issn.1001-506X.2021.09.32

• Co-frequency interference
• Single channel
• Time-domain convolutional network (TCN)
• Underdetermined blind source separation

Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite ground communication scene, a stacked time domain convolutional network (Stacked-TCN) separation method based on deep learning is proposed. Firstly, the coding feature representation is extracted from the mixed signal. Then, the deep feature mask of the source signal is trained through the time-domain convolution network. The mask of each signal source and the coding feature of the mixed signal are multiplied by Hadamard to obtain the coding feature representation of the source signal. Finally, 1-D convolution is used to decode the characteristics of the source signal to obtain the original waveform. In the experiment, the negative scale invariant source to noise ratio is used as the loss function of network training, that is, the evaluation index of single channel blind source separation performance. The results show that the Stacked-TCN method has better separation accuracy and noise robustness than the other four algorithms.

• 单位
  中国科学院大学