Aiming at the problem that the sea surface communication is seriously disturbed by atmospheric noise

this paper proposes a single sample polarization filter prediction model based on deep neural network

and studies its suppression effect on atmospheric noise in the sea surface short ground wave communication link. DNN neural network directly obtains the non-linear characteristics between information from a large amount of input data

which uses it to update the network parameters

and adjusts the model parameters to make the model reach the optimal state. Three types of atmospheric noise with different proporti

ons of pulse components are selected for simulation. The results show that the traditional algorithm and the DNN network model have basically the same effect on the signal error rate when the signal-to-noise ratio is about 0~15dB. When the bit error rate reaches 10

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the deep learning model improves the signal-to-noise ratio by about 5dB compared with the traditional algorithm. The experimental results verify the feasibility and accuracy of the neural network in predicting the direction of the single-sample polarization filter coefficients

which has good practical value.