基于深度学习的雷达干扰识别方法研究

针对复杂电磁环境下雷达干扰信号识别问题，从优化卷积神经网络结构的角度出发，本文提出了一种对卷积神经网络结构LeNet-5增加批量归一化层和改变激活函数的方法。该方法能够加速网络收敛，提升网络的学习效率。本文首先建立舰船目标模型，分析了噪声调幅干扰、噪声调频干扰、梳状谱干扰和无干扰的真实目标回波信号在时频域的差异，然后利用四种信号对舰船目标模型生成数据集，最后通过本文所提方法实现雷达干扰的自动识别。仿真结果表明：在全信噪比条件下，本文所提方法对四种信号的识别准确率达到98.1%，表明所提方法有着较好的稳定性和鲁棒性。

Research on radar jamming recognition method based on deep learning

To solve the problem of radar jamming signal recognition in complex electromagnetic environment, from the perspective of optimizing the convolution neural network structure, this paper proposes a method to add a batch normalization layer and change the activation function to the convolution neural network structure LeNet-5. This method can accelerate the network convergence and improve the network learning efficiency. In this paper, the ship target model is first established, and the differences in time-frequency domain between noise amplitude modulation jamming, noise frequency modulation jamming, comb spectrum jamming and the real target echo signal without jamming are analyzed. Then the data sets are generated for the ship target model by using four kinds of signals. Finally, the automatic recognition of radar jamming is realized by the method proposed in this paper. The simulation results show that under the condition of full signal-to-noise ratio (SNR), the recognition accuracy of the proposed method for four signals reaches 98.1%, indicating that the proposed method has good stability and robustness.