Low resolution radar target recognition method based on deep transfer learning

DOI:	10.11809/bqzbgcxb2023.11.031
Keywords:	deep learning; transfer learning; low resolution radar; small sample; target recognition
Abstract:	Aiming at the low efficiency of low resolution radar artificial target recognition, a radar automatic target recognition method based on deep transfer learning is proposed. In this method, sequence outline images of radar echo are used to construct aerial target dataset, and deep convolutional neural network is used to automatically extract the deep features in the echo data, and the radar target is classified and recognized. In order to solve the huge demand for the sample size of deep learning, the idea of transfer learning is introduced during the training of the classification model. The initial network model pre trained by the ImageNet data set is transferred to the radar target recognition task, and then the model parameters are fine tuned through the aerial target data set to realize the rough classification of air targets under the condition of small samples. The experimental results of the measured data show that the proposed method can classify and recognize the size and sorties of aerial targets more accurately under the condition of small samples and has good recognition performance.
Issue:	Vol. 44 No. 11 (2023)
Published:	2023-11-28
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