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Deep Learning Based Classification of Radar Spectral Maps

Tong Lin, Xin Chen, Xiao Tang, Ling He, Song He, and Qiaolin Hu
Air Force Early Warning Academy, Wuhan, China

Abstract—This paper discusses the use of deep convolutional neural networks for radar target classification. In this paper, three parts of the work are carried out: firstly, effective data enhancement methods are used to augment the dataset and address unbalanced datasets. Second, using deep learning techniques, we explore an effective framework for classifying and identifying targets based on radar spectral map data. By using data enhancement and the framework, we achieved an overall classification accuracy of 0.946. In the end, we researched the automatic annotation of image ROI (region of interest). By adjusting the model, we obtained a 93% accuracy in automatic labeling and classification of targets for both car and cyclist categories.

Index Terms—Radar spectral, deep learning, target recognition

Cite: Tong Lin, Xin Chen, Xiao Tang, Ling He, Song He, and Qiaolin Hu, "Deep Learning Based Classification of Radar Spectral Maps," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 10, No. 2, pp. 99-104, March 2021. Doi: 10.18178/ijeetc.10.2.99-104

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.