Radar HRRP based few-shot target recognition with CNN-SSD

The development of radar high resolution range profile(HRRP)non-cooperative targets recognition technology is mainly limited by two aspects: (1) Due to the low observation frequency of non-cooperative targets, the number of labeled HRRPs is insufficient, making non-cooperative HRRP based target recognition a typical few-shot recognition problem, which is still a hot and difficult issue without definite conclusion in the academia.(2) The existing HRRP based target recognition methods are mostly based on the hypothesis of complete dataset, making them mismatch with non-cooperative target recognition in few-shot setting.In this paper, we put aside the complete hypothesis and propose an HRRP based few-shot target recognition method with CNN-SSD.The proposed method first uses a complete training HRRP containing 45 classes of cooperative targets to learn an initial category-independent feature extractor, on the basis of which we further utilize the model sequential self-distillation mechanism to obtain a more generalized feature extractor.Finally, the generalization ability of the extracted features is evaluated on unseen non-cooperative targets during training.Experimental results on self-simulated HRRP dataset reveal that the proposed method can achieve an average recognition rates of 61.26%, 84.69% and 92.52% respectively when only 1, 5 and 10 annotated HRRPs of non-cooperative targets are available. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.