A Contrastive-Based Adversarial Training Algorithm for HRRP Target Recognition

In recent years, deep learning methods have significantly improved the recognition performance of high-resolution range profiles (HRRPs). However, the vulnerability of the deep network to attacks poses a serious threat to the security of radar target recognition systems. In this letter, an adversarial training algorithm based on contrastive learning is proposed that introduces the N-pair loss function and balances the feature space to smooth the decision boundary and improve the robustness. The experimental analysis demonstrates that the proposed method achieves better defense performance than the traditional adversarial training algorithms. The work presented in this letter provides an important step toward improving the security and reliability of deep learning-based radar recognition systems.