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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Main Authors: Tianjian Yang, You Chen, Siyi Cheng, Xing Wang, Xi Zhang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10844286/
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author Tianjian Yang
You Chen
Siyi Cheng
Xing Wang
Xi Zhang
author_facet Tianjian Yang
You Chen
Siyi Cheng
Xing Wang
Xi Zhang
author_sort Tianjian Yang
collection DOAJ
description 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 to the aircraft’s survivability. Therefore, this paper proposes a method of JTS based on generative adversarial imitation learning (GAIL). Firstly, the agent learns from expert strategies to achieve high reward returns, avoiding wasted time from unguided exploration, thereby ensuring that the agent maintains a good jamming effect throughout its application process. Secondly, based on generative adversarial theory, the discriminator measures the difference between the generated and expert strategies. This difference is used as an internal reward to assist in updating the neural network parameters, effectively reducing the complexity of reward function design. Finally, through case analysis, it can be observed that using the GAIL algorithm can achieve rewards close to those of the expert strategy. When applied online, it does not rely on accurate predictions or precise modeling of the external environment, allowing for quick real-time decision-making. Additionally, its performance surpasses that of traditional JTS strategies.
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issn 2169-3536
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spelling doaj-art-ecbda6bd3183415a8033505ff527b3902025-01-31T00:01:44ZengIEEEIEEE Access2169-35362025-01-0113171101711910.1109/ACCESS.2025.353101610844286Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation LearningTianjian Yang0https://orcid.org/0009-0007-8585-4994You Chen1Siyi Cheng2Xing Wang3Xi Zhang4Aviation Engineering School, Air Force Engineering University, Xi’an, ChinaAviation Engineering School, Air Force Engineering University, Xi’an, ChinaAviation Engineering School, Air Force Engineering University, Xi’an, ChinaAviation Engineering School, Air Force Engineering University, Xi’an, ChinaAviation Engineering School, Air Force Engineering University, Xi’an, ChinaIn 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 to the aircraft’s survivability. Therefore, this paper proposes a method of JTS based on generative adversarial imitation learning (GAIL). Firstly, the agent learns from expert strategies to achieve high reward returns, avoiding wasted time from unguided exploration, thereby ensuring that the agent maintains a good jamming effect throughout its application process. Secondly, based on generative adversarial theory, the discriminator measures the difference between the generated and expert strategies. This difference is used as an internal reward to assist in updating the neural network parameters, effectively reducing the complexity of reward function design. Finally, through case analysis, it can be observed that using the GAIL algorithm can achieve rewards close to those of the expert strategy. When applied online, it does not rely on accurate predictions or precise modeling of the external environment, allowing for quick real-time decision-making. Additionally, its performance surpasses that of traditional JTS strategies.https://ieeexplore.ieee.org/document/10844286/Jamming type selectionreinforcement learningimitation learninggenerative adversarial network
spellingShingle Tianjian Yang
You Chen
Siyi Cheng
Xing Wang
Xi Zhang
Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning
IEEE Access
Jamming type selection
reinforcement learning
imitation learning
generative adversarial network
title Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning
title_full Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning
title_fullStr Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning
title_full_unstemmed Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning
title_short Optimization of Jamming Type Selection for Countering Multifunction Radar Based on Generative Adversarial Imitation Learning
title_sort optimization of jamming type selection for countering multifunction radar based on generative adversarial imitation learning
topic Jamming type selection
reinforcement learning
imitation learning
generative adversarial network
url https://ieeexplore.ieee.org/document/10844286/
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AT siyicheng optimizationofjammingtypeselectionforcounteringmultifunctionradarbasedongenerativeadversarialimitationlearning
AT xingwang optimizationofjammingtypeselectionforcounteringmultifunctionradarbasedongenerativeadversarialimitationlearning
AT xizhang optimizationofjammingtypeselectionforcounteringmultifunctionradarbasedongenerativeadversarialimitationlearning