In order to solve the problem that the traditional signal sorting method has poor modulation signal recognition effect when the signal-to-noise ratio is too low,a residual network(ResNet-Peca)integrating efficient channel attention mechanism(ECA)and position attention mechanism(PAM)is proposed.This network can simultaneously obtain the feature weights of channel and position dimensions to improve the feature learning ability of the network.The study results show that the cognition accuracy of the residual network with attention mechanism(ResNet-Peca)is about 1.5%higher than that of the ResNet-ECA method,about 1.6%higher than that of the ResNet-PAM method,and about 3.8%higher than that of the CNN method.
signal sortingattention mechanismresidual networkconvolution neural network
NETL
NSTL
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