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Deep Transfer Learning Based Method for Radar Automatic Recognition with Small Data Size

To address the challenge of radar high resolution range profile (HRRP) target recognition with a small sample size, deep transfer learning-based method is proposed. Firstly, a pre-trained model suitable for small sample size is designed, and a loss function that can improve the generalization performance of the pre-trained model is proposed. The pre-trained model is trained from scratch with source domain data (the simulated HRRP dataset). On the basis of the pre-trained model, the structures of a fully connected layer and an output layer are reset and initialized to constitute a fine-tuned model. During fine-tuning, to solve the problems of the dissatisfactory recognition performance due to the small data size and class imbalance of the measured HRRP dataset, a loss function that can reduce recognition deviations induced by imbalanced samples between classes and improve feature separability is proposed. With a small sample size, compared with a convolutional neural network model trained from scratch, the proposed method has an increased convergence rate and improved model stability while having improved recognition accuracy.