海面短波地波通信中基于DNN神经网络的单样本极化滤波器预测研究

针对海面通信受大气噪声干扰严重的问题，该文提出一种基于DNN（Deep Neural Network）神经网络的单样本极化滤波器预测模型，研究其对海面短波地波通信链路中的大气噪声的抑制作用.与传统算法不同，DNN神经网络直接从大量输入数据获取信息间的非线性特性，并以此更新网络参数，通过对模型参数调整使得模型达到最优状态.选择三种脉冲成分比例不同的大气噪声进行仿真，结果表明传统算法与DNN网络模型在低信噪比约0~15dB时对信号的误码率影响基本一致，在高信噪比约超过15dB，误码率达到10^-4时，深度学习模型比传统算法所需信噪比显著提高约5dB.实验结果验证了神经网络在单样本极化滤波器预测方向的可行性与准确性，具有很好的实用参考价值.

Research on Single Sample Polarization Filter Based on Deep Neural Network in Short Ground-Wave Communication over the Sea Surface

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 proportions 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^-4,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.