Radar Signal Recognition Based on CNN With a Hybrid Attention Mechanism and Skip Feature Aggregation

Radar signal recognition is of great significance for threat analysis and intelligence support in radar electronic warfare. However, it remains a challenging task due to: 1) the variety of radar signal modulation types; 2) the increasingly saturated electromagnetic environment and the more serious influence of noise interference; and 3) the intrinsic real-time requirement of this application. In this article, a novel automatic modulation recognition algorithm based on the joint architecture of convolutional neural network (CNN) and the support vector machine (SVM) is proposed to comprehensively address these challenges. In particular, this algorithm first employs time-frequency (TF) analysis to better show the pulse representation in the TF domain. Then, a well-designed DCNN equipped with a hybrid attention (HA) mechanism and skip feature aggregation (SFA) is used to automatically learn the feature representations of distinctiveness from TF images. HA is designed to quickly capture the important information of local parts in sight and enhance the ability of the backbone network to mine high-level features. SFA is applied to selectively aggregate features among channels for self-monitoring, which improves the expression and generalization ability. Sixteen types of radar signals are used to verify the feasibility and effectiveness of this algorithm. The experimental evaluation shows that the proposed method not only achieves competitive accuracy with better timeliness than state-of-the-art methods but also achieves effective identification of most of the signal modulation types.