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Affiliations:
1:
College of Electronic and Information Engineering , Nanjing University of Aeronautics and Astronautics , Nanjing , China ;
2:
School of Software Engineering , Chongqing University of Posts and Telecommunications , Chongqing , China
Source: IET International Radar Conference (IET IRC 2020),
2021
p.
425 – 430
High resolution range profile (HRRP) target recognition based on deep learning methods is mainly dedicated to changing the 2-Dimensional (2-D) convolutional neural network (CNN) framework into a 1-Dimensional (1-D) feature extractor. In this paper, a new algorithm called triple gramian angular field with CNN (TGAF-CNN) is proposed for HRRP recognition in the low signal-to-noise (SNR) condition. Different from traditional methods, HRRP is transformed into a 2-D image by TGAF. Compared with GAF feature, TGAF feature extend channels to achieve feature fusion which can extract more temporal information for higher recognition precision and better robustness. Then, 2-D CNN is used for recognition. Experiments based on real data from gun models indicate that TGAF-CNN can improve the HRRP recognition accuracy by about 4% compared to conventional deep learning methods.