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access icon free Radar emitter classification based on unidimensional convolutional neural network

  • Author(s): Jun Sun 1, 2, 3 ; Guangluan Xu 2, 3 ; Wenjuan Ren 2, 3 ; Zhiyuan Yan 1, 2, 3
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  • Source: Volume 12, Issue 8, August 2018, p. 862 – 867
    DOI:  10.1049/iet-rsn.2017.0547 , Print ISSN 1751-8784, Online ISSN 1751-8792
© The Institution of Engineering and Technology
Received 27/11/2017, Accepted 10/04/2018, Revised 08/04/2018, Published 12/04/2018

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%.

Inspec keywords: radar signal processing; Big Data; learning (artificial intelligence); electronic warfare; electrical engineering computing; electromagnetism; neural nets; sequences; signal classification

Other keywords: REC; high-dimension equal length sequence; deep learning model; big data problem; electromagnetism environment; electronic warfare system; unidimensional convolutional neural network; U-CNN model; encoding method; radar emitter classification; EW system; radar signal classification

Subjects: Knowledge engineering techniques; Signal processing and detection; Radar equipment, systems and applications; Digital signal processing; Electrical engineering computing; Neural computing techniques

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