基于融合Attention机制的残差神经网络雷达干扰感知方法

A Radar Jamming Perception Method Based on Residual Neural Network and Attention Mechanism

针对复杂电磁环境中,雷达干扰信号在低干噪比条件下识别率低,干扰样本难以大量获取用于训练的问题,提出一种基于Attention机制、迁移学习和残差神经网络的雷达干扰感知方法。模型以干扰与回波的时频域数据为输入,自动提取特征并做出类型判断。实验结果表明,模型实现了低干噪比、小样本训练条件下的雷达干扰有效感知识别,Attention机制和迁移学习能够有效提高感知识别正确率,识别性能相较于传统机器学习模型与未改进过的神经网络模型鲁棒性更好,更加精确。

In the complex electromagnetic environment,the recognition rate of radar jamming signal is low under the condition of low Jamming Noise Ratio(JNR),and it is difficult to obtain a large number of jamming samples for training.To solve the problem,a radar jamming perception method based on Attention mechanism,transfer learning and residual neural network is proposed.The model takes the time-frequency domain data of interference and echo as input,automatically extracts features and makes type judgment.The experimental results show that the model realizes the effective perceptual recognition of radar jamming under the condition of low JNR and small-sample training.The Attention mechanism and transfer learning can effectively improve the accuracy of perceptual recognition.The recognition performance is more robust and accurate than that of the traditional machine learning model and the unmodified neural network model.