Abstract:

In electronic warfare, radar systems are confronted with significant threats within the complex electromagnetic environment, particularly from the prevalent method of blanket jamming that impairs radar’s detection capabilities. Frequency agility is recognized as an effective anti-jamming method against blanket jamming in practice. However, the traditional frequency agility selects the carrier frequency randomly, which highly limits the potential for robust anti-jamming capabilities, despite offering sufficient optimization space to improve the radar performance. This paper proposes an antijamming decision strategy based on the comb gradient-bandits algorithm (CGBA) to optimize decisions for frequency agility radar under blanket jamming conditions. In this strategy, the reward value is defined as the signal to interference plus noise ratio (SINR) of the radar in each training round, and the radar selects the frequency channel with the highest reward value as the new operational channel. The proposed method has dynamic adaptive ability according to the jamming status, which enables the radar to update its frequency channel in real-time. Theoretical analysis and experimental results show that the proposed model improves the radar’s ability to select anti-jamming strategies to a certain extent.

Key words: frequency agility, comb gradient-bandits (CGBA), frequency channel, anti-jamming strategy