Open AccessFeature Extraction of Radar Echo Image Based on Improved Convolutional Neural Network
Author(s) -
Chenhao Zhang,
Hongquan Li,
Feng Xun,
Tang Jingmian
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012063
Subject(s) - convolutional neural network , artificial intelligence , computer science , feature extraction , convolution (computer science) , radar , pattern recognition (psychology) , feature (linguistics) , dropout (neural networks) , identification (biology) , image (mathematics) , echo (communications protocol) , radar imaging , data set , artificial neural network , computer vision , machine learning , telecommunications , computer network , linguistics , philosophy , botany , biology
Feature extraction of radar echo image is an important part of identification and recognition of air targets. In recent years, with the rapid development of deep learning, new solutions are provided for it. In this paper, the convolution neural network (CNN) is applied to feature extraction. Based on the classic CNN model, Adam is used to update the model parameters, dropout is used to prevent over fitting, and an improved CNN model is constructed. Then, the radar echo image data set is used to train the model, so as to extract target features and classify them. Simulation results show that the accuracy of the improved model is 99%, and the training speed is greatly improved. Different from the traditional extraction method which relies on manual experience, the improved CNN can improve the efficiency of feature extraction of radar echo image and lay a solid foundation for further research and identification of air targets.