A Prior-Knowledge-Guided Neural Network Based on Supervised Contrastive Learning for Radar HRRP Recognition
Qi Liu, Xinyu Zhang, Yongxiang Liu
Abstract
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. Firstly, 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 targetaspect 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.