基于改进DCGAN的毫米波雷达相互干扰时频图像生成研究——以生成样本对CNN干扰抑制模型性能影响为例

Research on Generation of MMW Radar Mutual Interference Time-Frequency Image Based on Improved DCGAN——A Case of Performance Effect of the Generated Samples on the CNN Interference Suppression Model

采用深度学习方法抑制毫米波雷达时频域干扰面临着实测样本不足的问题,数据集的大小和质量会影响模型的干扰抑制性能和泛化性能。文章提出一种改进DCGAN算法来生成毫米波雷达时频域干扰图像,以扩充训练深度学习干扰抑制模型的实测训练样本。改进DCGAN算法对网络结构做出调整,使用带梯度惩罚的Wasserstein距离替代DCGAN的损失函数。实验结果表明,在原始仿真数据集中加入改进DCGAN算法生成的样本,能够有效提高CNN模型的干扰抑制性能。

Using deep learning method to suppress the MMW radar time-frequency field interference is faced with the problem of lacking actual measurement samples.The size and quality of datasets will affect the interference suppression and generalization performance of the model.This paper proposes an improved DCGAN algorithm to generate the time-frequency field interference images of the MMW radar to expand the actual measurement training samples for training deep learning interference suppression model.The improved DCGAN algorithm adjusts the network structure and the Wasserstein distance with gradient penalty is used to replace the loss function of DCGAN.Experimental results show that adding samples generated by the improved DCGAN algorithm into the original simulation dataset can effectively improve the interference suppression performance of CNN model.