Machine Learning Aided Electronic Warfare System

Abstract

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.