Details

Autor(en) / Beteiligte

• Wang, Husheng
• Chen, Baixiao
• Ye, Qingzhi

Titel

Design of anti‐jamming decision‐making for cognitive radar

Ist Teil von

• IET radar, sonar & navigation, 2024-03, Vol.18 (3), p.514-531

Ort / Verlag

Wiley

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed. This work proposes an intelligent anti‐jamming decision‐making scheme designed for the cognitive jamming to an anti‐jamming interaction mode. This scheme is suitable for scenarios with varying jamming types and the capability to predict jamming behaviour.