Abstract:

Aiming at the low detection rate of traditional constant false alarm rate (CFAR) detection methods, a deep learning radar target detection method based on you only look once (YOLO) is proposed. Firstly, the radar target image dataset is constructed by using the original radar image formed by in-phase/quadrature (I/Q) data matching filtering. Then, the network structure, feature fusion strategy, and loss function of the YOLO detection model are improved to improve the accuracy of the model. And the idea of transfer learning is introduced to extract image features using the pre-trained deep learning network, which reduced the requirement of the detection model on the training sample size. Finally, the YOLO target detection method is experimentally verified on the self-built dataset. The experimental results on the measured data of the primary surveillance radar show that, compared with the traditional CFAR detection method and the two-stage faster region convolutional neural networks (R-CNN) detection method, the target detection rate of the proposed method is greatly improved, the false alarm rate is significantly reduced, and the real-time detection is realized.

Key words: primary surveillance radar, deep learning, target detection, you only look once (YOLO)