# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 1 Introduction  
Efficient and high-accuracy radar-signal modulation recognition has emerged as a pivotal requirement in modern electronic warfare [1, 2]. With the ever-increasing complicated electromagnetic environment, the interceptors are likely to be affected by the existing multiple potential emitters and result in an overlapping of the radar signals. As the first step for electronic reconnaissance, the precise detection of the signal modulation can provide promising evidence for the subsequent parameter estimation and jamming implementation [3]. Therefore, automatically recognizing the modulation of overlapping radar signals was of great significance, and has become an essential topic in signal processing. Radar-signal modulation recognition techniques have been investigated in recent decades, and mainly contain radar signal feature extraction and modulation recognition [4, 5]. Previous research mainly used the handcrafted feature to train a multi-class classifier of single-component radar signals [6]. However, the conventional extracted features in single-component radar signal recognition, such as high-order moments and cumulants [7] and power spectral density, as well as instantaneous frequency and phase, were inadequate in the circumstance of overlapping radar signals. Moreover, the potential hybrid category was rather random, and the computational features led to poor generalization performance. Because of the enormous advantages of deep learning, multi-class classification algorithms based on deep learning have been proposed for recognizing dual-component radar signals [8, 9], which shed light on the capability of highly complicated feature representation [10]. Prior work has largely proved that the extracted representationsthrough deep learning models were more robust and efficient than the handcrafted features of computer vision, which were also potentially applicable to the signal process. The convolutional neural network (CNN) has been adopted for recognizing related tasks and achieved encouraging performance on radar-signal modulation recognition [11; 12; 13]. That is because it possessed a robust learning ability feature and higher classification performance compared with the traditional methods, especially for converting radar signals into time-frequency images and automatically extracting various feature details of images. In particular, ResNet [3; 14], U-shaped network (U-Net) [15], Asymmetric Convolution Squeeze-and-Excitation [16], and their variants have witnessed remarkable success in radar-signal modulation recognition. ResNet improved the network performance by increasing depth and using residual connections, while it took a lot of computational resources and may overfit. U-Net reduced the size of the feature map by using encoders to superimpose convolution and pooling operations, while it reduced the efficiency and lost spatial information. All of the architecture benefited from a skip connection which incorporated semantic features with fine-grained features. However, the radar-signal modulation recognition method based on CNN makes it difficult to effectively mine the characteristics of dual-component radar signals. Recently, the transformer has become a deep learning architecture specifically designed to work with sequential data. The main functions include sequence modeling and understanding, efficient parallel computing, and long-distance dependency modeling. Therefore, it has also been utilized in computer vision [17]. In [18], a dynamic transformer was proposed for configuring some tokens, while it would require higher computation requirements. Following this structure, a swin transformer was proposed [19], which computed self-attention within non-overlapping local windows and leveraged the shifted window partition to build connections among the windows of each preceding layer. Although the swin transformer model has made great progress in image classification [20], it has not been utilized in radar-signal modulation recognition.