Design of anti-jamming decision-making for cognitive radar

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.image