Dual TOA and Signal Type Prediction for Electronic Warfare Applications
Brian Egolf, Jason R. Pennington, Chi‐Hao Cheng · IEEE Transactions on Aerospace and Electronic Systems · 2024
Modern military radars are sophisticated machines emitting sequences of signals for surveillance and tracking purposes. Advanced radars, known as multifunction radars (MFRs), can send out a variety of signals and switch between different states in a very short amount of time. To effectively jam against an MFR, the electronic warfare (EW) systems need to predict the next incoming signal and its arrival time with high accuracy. Recent years have seen an explosion of research in the application of machine learning in both radar and EW applications. This article proposes a unified machine learning model for predicting pulse-repetition-interval values with the next radar signal's time of arrival, as well as predicting the characteristics of the signal that the radar will emit. The simulation results demonstrate the effectiveness of the proposed method.