A Prior-Knowledge-Guided Neural Network Based on Supervised Contrastive Learning for Radar HRRP Recognition

Radar automatic target recognition (RATR) based on high-resolution range profiles (HRRPs) has received intensive attention in recent years. The data-driven HRRP recognition methods based on neural networks have achieved outstanding performance with a complete target-aspect template library. However, such methods can barely achieve satisfactory performance under the condition of incomplete target-aspects or low signal-to-noise ratio. In order to solve this problem, a prior-knowledge-guided neural network (PriorK-NN) is proposed for HRRP target recognition, which incorporates prior-knowledge about scattering centers and target-aspects into neural networks. First, by combining the physical generative mechanism of HRRP with neural networks, we proposed an interpretable scattering center layer (SC-layer). The SC-layer can effectively reduce the adverse effects of noise and extract the location and intensity of target dominant scattering centers. In addition, we proposed a loss function named target-aspect supervised contrastive loss based on the prior-knowledge about target-aspects. By using the proposed loss function, clusters of HRRPs belonging to the same class but with different target-aspects are pulled together while simultaneously pushing apart clusters of HRRPs from different classes. Therefore, the proposed loss function can improve the recognition performance under the condition of incomplete target-aspects. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset validated the effectiveness of our proposed method on noise-corrupted HRRP recognition and demonstrated superior performance compared with other HRRP recognition methods under the condition of incomplete target-aspects.