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http://dx.doi.org/10.9766/KIMST.2020.23.1.001

Estimation of Jamming Parameters based on Gaussian Kernel Function Networks  

Hwang, TaeHyun (Department of Mathematics, SungKyunKwan University)
Kil, Rhee Man (College of Computing, SungKyunKwan University)
Lee, Hyun Ku (EW(Electronic Warfare) R&D Lab, LIG Nex1 Co. Ltd.)
Kim, Jung Ho (EW(Electronic Warfare) R&D Lab, LIG Nex1 Co. Ltd.)
Ko, Jae Heon (EW(Electronic Warfare) R&D Lab, LIG Nex1 Co. Ltd.)
Jo, Jeil (The 2nd Research and Development Institute, Agency for Defense Development)
Lee, Junghoon (The 2nd Research and Development Institute, Agency for Defense Development)
Publication Information
Journal of the Korea Institute of Military Science and Technology / v.23, no.1, 2020 , pp. 1-10 More about this Journal
Abstract
Effective jamming in electronic warfare depends on proper jamming technique selection and jamming parameter estimation. For this purpose, this paper proposes a new method of estimating jamming parameters using Gaussian kernel function networks. In the proposed approach, a new method of determining the optimal structure and parameters of Gaussian kernel function networks is proposed. As a result, the proposed approach estimates the jamming parameters in a reliable manner and outperforms other methods such as the DNN(Deep Neural Network) and SVM(Support Vector Machine) estimation models.
Keywords
Electronic Warfare; Radar Pattern; Adaptive Jamming; Gaussian Kernel Function Network;
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