Radar HRRP Target Recognition Based on Deep One-Dimensional Residual-Inception Network

Abstract

A novel radar target recognition method based on the deep one-dimensional residual-inception network is proposed for a high-resolution range profile (HRRP). The traditional methods based on shallow models can hardly extract the complete information of the targets HRRP from different angles. The deep models, such as sparse autoencoder, have been adopted to solve this problem. However, these deep models with a huge amount of parameters require more training samples to guarantee the generalization performance. To solve the above-mentioned problem, a model based on the one-dimensional convolutional kernel and a pooling layer is proposed. It is worth noting that the one-dimensional convolutional kernel and pooling operation have the potential to overcome the translation sensitivity and target aspect sensitivity of the HRRP, and both of them can greatly reduce the parameters and improve the generalization performance of the model. In addition, a new loss function is proposed to further enhance the separability of features. The experimental results show that compared with other four deep models, the proposed model can achieve a good performance in recognition accuracy and robustness.