Underwater acoustic communication signal recognition based on deep learning
Underwater acoustic communication signal recognition is an important prerequisite for underwater acoustic communication reconnaissance and countermeasures and plays an important role.However,traditional underwater acoustic communication signal recognition methods are usually based on signal processing and pattern recognition technology.The selection and extraction of features mainly rely on the professional knowledge and experience of domain experts,which is highly subjective and may not be able to use more complex signal characteristics.In this paper,the convolutional neural net-work in deep learning is used to automatically extract the characteristics of communication signals.First,the network is trained using the simulated data,and then the algorithm network is tested using the simulation and lake test data.The results show that when the SNR is 5 dB,the recognition rates of seven underwater communication signals,including the 2ASK,4ASK,BPSK,QPSK,2FSK,4FSK and OFDM,can reach over 90％,and the average recognition rate of the seven types of communication signal types tested on the lake reaches 97.9％,which proves the good tolerance of the algorithm.At the same time,the comparison based on higher-order cumulant and deep learning method confirms the significant advantages of the proposed method.
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