To correctly classify the specific protocol signal

a signal recognition model with adaptive learning and automatic feature extraction ability is proposed. This model is based on the deep residual network

which can solve the drawbacks

such as

the poor quality of the intercepted communication signal

the complex condition of the short wave channel

and the low recognition rate of the single feature. After analyzing the visual characteristic of communication protocol signal with special structure in time frequency domain

the time frequency gray-images are obtained and utilized to train the deep residual network. This method does not need much prior knowledge and is insensitive to signal quality. Moreover

it can process the intermediate-frequency signal directly. Due to these advantages

the algorithm is suitable for engineering application. Simulation results show that

when the deep residual network reaches its steady status

the proposed model can accurately identify the protocol. And it is also proved effective even at complex circumstance where the multipath fading and the Doppler shift exist

the signal-to-noise ratio is low

and the interference is strong.