Radar emitter classification based on unidimensional convolutional neural network. Issue 8 (1st August 2018)

• Record Type: Journal Article
• Authors: Sun, Jun; Xu, Guangluan; Ren, Wenjuan; Yan, Zhiyuan
• Is Part Of: IET radar, sonar & navigation. Volume 12:Issue 8(2018)
• Page Start: 862
• Page End: 867
• Publication Date: 2018-08-01
• Subjects: electronic warfare -- signal classification -- neural nets -- radar signal processing -- electromagnetism -- Big Data -- learning (artificial intelligence) -- sequences -- electrical engineering computing; radar emitter classification -- unidimensional convolutional neural network -- REC -- electronic warfare system -- EW system -- radar signal classification -- electromagnetism environment -- big data problem -- encoding method -- high‐dimension equal length sequence -- deep learning model -- U‐CNN model; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2017.0547
• Languages: English
• ISSNs: 1751-8784

Abstract

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%.