NDLI: Radar emitter classification based on unidimensional convolutional neural network

Content Provider	IET Digital Library
Author	Sun, Jun Xu, Guangluan Ren, Wenjuan Yan, Zhiyuan
Abstract	Radar emitter classification (REC) is an essential part of electronic warfare (EW) systems. In REC tasks, after deinterleaving, the intercepted radar signals are classified into specific radar types. With new radar types arising and the electromagnetism environment getting complicated, REC has become a big data problem. Meanwhile, there exist inconsistent features among samples. These two problems can affect the performance of classification. In this work, first, the authors designed a novel encoding method to deal with the inconsistent features. High-dimension sequences of equal length are generated as new features. Then a deep learning model named unidimensional convolutional neural network (U-CNN) is proposed to classify the encoded high-dimension sequences with big data. A large and complex radar emitter's dataset is used to evaluate the performance of the U-CNN model with the encoding method. Experiments show that the authors' proposal gains an improvement of 2–3% in accuracy compared with the state-of-the-art methods, while the time consumed for identifying 45,509 emitters is only 1.95 s using a GPU. Specifically, for 12 indistinguishable radars, the classification accuracy is improved about 15%.
Starting Page	862
Ending Page	867
Page Count	6
ISSN	17518784
Volume Number	12
e-ISSN	17518792
Issue Number	Issue 8, Aug (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2017.0547
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-04-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Big Data Big Data Problem Deep Learning Model Digital Signal Processing Electrical Engineering Computing Electromagnetism Electromagnetism Environment Electronic Warfare Electronic Warfare System Encoding Method EW System High-dimension Equal Length Sequence Knowledge Engineering Technique Learning in AI Neural Computing Technique Neural Nets Radar Emitter Classification Radar Equipment Radar Signal Classification Radar Signal Processing REC Sequence Signal Classification Signal Processing And Detection System And Application U-CNN Model Unidimensional Convolutional Neural Network
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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