Single Channel Blind Source Separation of Complex Signals Based on Spatial‐temporal Fusion Deep Learning

Blind Source Separation (BSS) of complex signals composed of radar, communication and jamming signals is the first step in an integrated electronic system, which requires higher accuracy of separation. However, the traditional Single-Channel Blind Source Separation (SCBSS) method has low separation accuracy and poor robustness. Aiming at this problem, this paper proposes a SCBSS method based on spatial-temporal fusion deep learning model. This is a deep neural network model, which realizes spatial-temporal of mixed signals by integrating Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). Convolutional Neural Network is used to extract spatial features from input sequences, and BiLSTM is used to mine timing rules of signals. A batch normalisation layer and a dropout layer are added to improve stability and prevent overfitting. The experiments show that the average similarity coefficient of the separated signals is above 0.99 and the Signal-Distortion Ratio (SDR) is up to 27 dB without noise. When the Signal-Noise Ratio is 0-20 dB and Jamming-Signal Ratio is 15 dB, the SDR is 5-30 dB higher than the traditional methods and the single network structure deep learning methods.