Contrastive Learning for Radar Target Recognition Based on HRRP

In recent years, target recognition based on high-resolution range profile (HRRP) has been widely studied in the field of radar automatic target recognition. Compared with traditional methods, deep network model can automatically obtain the deep features of the target and is widely used, but expensive and time-consuming labeling is a difficult task. Inspired by the idea of contrastive learning, a self-supervised framework for learning target features based on double contrastive loss, namely contrastive learning with discriminant loss (CLDL) is proposed. Specifically, we minimize the absolute distance between positive sample pairs and maximize the absolute distance between negative sample pairs through constraints to ensure the proximity of two related samples and the discrimination of unrelated samples under the same HRRP signal. At the same time, the peak clipping strategy is proposed to preserve the useful information of the signal to the greatest extent during data augmentation. The experimental results show that the recognition performance of CLDL is better than the five mainstream unsupervised learning algorithms.