In order to solve the problem that the traditional method of communication transmitter individual identification ignores the nonlinearity, shallow features and timing characteristics of the signal, this paper proposes a method of communication transmitter individual identification based on GAF-ResNet. This method uses the Gramian Angular Field to convert timeseries signals into two-dimensional images of time-domain signals, which takes better advantages of the dependence and correlation of time series. The residual block of the residual network is used to realize the fusion between shallow features and deep features. The experimental results show that the best network model trained by this method has a recognition rate of 92.7%, which proves the effectiveness of the method.
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