Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning

Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning

In recent years, deep reinforcement learning (DRL) has made some progress in jamming type selection (JTS). However, during the training process of the agent, exploration of the action space is necessary, which leads to poor jamming effects in the early stages of training, posing a significant threat...

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Bibliographic Details
Main Authors: Tianjian Yang, You Chen, Siyi Cheng, Xing Wang, Xi Zhang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Jamming type selection
reinforcement learning
imitation learning
generative adversarial network
Online Access:https://ieeexplore.ieee.org/document/10844286/
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