Deep Fusion for Radar Jamming Signal Classification Based on CNN
Guangqing Shao, Yushi Chen, Yinsheng Wei
Abstract
The accurate classification of radar jamming signal is a core step of anti-jamming. Recently, convolutional neural network (CNN) based methods have shown their powerfulness in signal processing. In this paper, a deep fusion method based on CNN is proposed to classify jamming signal acting on pulse compression radar. The proposed method consists of three subnetworks (i.e., 1D-CNN, 2D-CNN, and fusion network). 1D-CNN is used to extract deep features of original radar jamming signal. Meanwhile, in order to extract the time-frequency features, short time Fourier transform (STFT) is applied to jamming signal to obtain time-frequency spectrograms. Then, 2D-CNN is used to extract deep time-frequency features, which are useful for further features fusion processing. Fusion network is used to deeply fuse the extracted features of the aforementioned CNNs and softmax is used to finish the task of radar jamming signal classification. In addition, in order to alleviate the problem of overfitting and improve the generalization ability of proposed model, soft label smoothing is proposed. The experimental results reveal that the proposed method provides competitive results in term of classification accuracy.