2019, 41(12):35-39.
Abstract:
Aircraft target recognition is an important key technique of ground information systems. Recent hot deep learning methods, e. g. convolutional neural network, have shown superior performance for image recognition tasks. However, training a convolutional neural network requires a number of annotated samples to estimate numerous model parameters, which restricts its application to radar target recognition. Aiming at the small sample issue in aircraft target recognition, the transfer learning technique that suits the limited data is used in this study, combined with the advantages of convolutional neural network, an initial convolutional neural network has been trained in advance on big samples of radar high resolution range profiles, and then the model parameters are fine-tuned based on the current aircraft target recognition task. Experimental results of measured data show that compared to the results of merely applying convolutional neural networks, the proposed method can obviously increase the recognition accuracy rate, which validate the effectiveness of the proposed method.