A cooperative jamming mode adjustment method based on Multi-Agent reinforcement learning

With the advancement of multifunctional netted radar systems (NRS), traditional jamming decision-making strategies struggle to adapt to the nonlinear challenges of dynamic electromagnetic countermeasures environments, particularly against multifunctional NRS. To address this, we propose a Multi-agen...

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Vydáno v:	Ain Shams Engineering Journal Ročník 16; číslo 11; s. 103672
Hlavní autoři:	Wang, Jieling, Liu, Yanfei, Li, Chao, Yang, Dongdong, Yin, Qingshan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.11.2025 Elsevier
Témata:	Alternating training Jamming mode adjusting Multi-agent reinforcement learning algorithm Netted Radar System (NRS) Non-cooperative adversarial scenario model Nonlinear problems Multi-agent reinforcement learning algorithm Nonlinear problems Non-cooperative adversarial scenario model Alternating training Jamming mode adjusting Netted Radar System (NRS)
ISSN:	2090-4479
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Popis
Shrnutí:	With the advancement of multifunctional netted radar systems (NRS), traditional jamming decision-making strategies struggle to adapt to the nonlinear challenges of dynamic electromagnetic countermeasures environments, particularly against multifunctional NRS. To address this, we propose a Multi-agent Joint Collaborative Jamming Adjustment Method (MJCJMA). Firstly, a non-cooperative adversarial scenario model is constructed, employing an improved snow melting algorithm (GPSAO-LSSVM) for radar threat pre-evaluation. And a threat quantification model is developed using enhanced entropy weighting (IEWM) and improved TOPSIS (ITOPSIS). Then, a multi-agent reinforcement learning algorithm is designed, integrating prioritized experience replay, entropy regularization, and reward centering to improve efficiency and stability. Furthermore, an alternating training strategy is proposed, which significantly accelerates the convergence process. Extensive simulation results validate the superiority of MJCJMA, which significantly reducing radar detection probability (96.25% vs. baseline, 47.51% vs. non-alternating training) and threat levels, thereby enabling intelligent jamming decisions in adversarial scenarios.
ISSN:	2090-4479
DOI:	10.1016/j.asej.2025.103672