Machine Learning Aided Electronic Warfare System

被引：2

作者：

McWhorter, Tanner ^{[1
]}

Morys, Marcin ^{[2
]}

Severyn, Stacie ^{[2
]}

Stevens, Sean ^{[2
]}

Chan, Louis ^{[2
]}

Cheng, Chi-Hao ^{[1
]}

机构：

[1] Miami Univ, Dept Elect & Comp Engn, Oxford, OH 45056 USA

[2] US Air Force, Res Lab, Dayton, OH 45433 USA

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Radar; Fuzzy logic; Decision trees; Machine learning; Entropy; Radar countermeasures; Training data; Electronic warfare; machine leaning; fuzzy logic; decision tree; ID3; Long Short-Term Memory (LSTM);

D O I：

10.1109/ACCESS.2021.3093569

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this article, a machine learning aided electronic warfare (EW) system is presented and the simulation results are discussed. The developed EW system uses an automatic decision tree generator to create engagement protocol and a fuzzy logic model to quantify threat levels. A long-short term memory (LSTM) neural network was also trained to predict the next signal set of multifunction radars. The simulation results demonstrate the effectiveness of the developed EW system's ability to engage multiple multifunction radars.

引用

页码：94691 / 94699

页数：9

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