GA-Dueling DQN Jamming Decision-Making Method for Intra-Pulse Frequency Agile Radar

GA-Dueling DQN Jamming Decision-Making Method for Intra-Pulse Frequency Agile Radar

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Optimizing jamming strategies is crucial for enhancing the performance of cognitive jamming systems in dynamic electromagnetic environments.The emergence of frequency-agile radars, capable of changing the copyright frequency within or between pulses, poses significant challenges Figure Skating - Clothing - Tights for the jammer to make intelligent decisions and adapt to the dynamic environment.This paper focuses on researching intelligent jamming decision-making algorithms for Intra-Pulse Frequency Agile Radar using deep reinforcement learning.

Intra-Pulse Frequency Agile Radar achieves frequency agility at the sub-pulse level, creating a significant frequency agility space.This presents challenges for traditional jamming decision-making methods to rapidly learn its changing patterns through interactions.By employing Gated Recurrent Units (GRU) to capture long-term dependencies in sequence data, together with the attention mechanism, this paper proposes a GA-Dueling DQN (GRU-Attention-based Dueling Deep Q Network) method for jamming frequency selection.

Simulation results indicate that the proposed method outperforms traditional Q-learning, DQN, and Dueling DQN methods in terms of jamming effectiveness.It exhibits the fastest convergence speed and reduced reliance on 1/6th Box Set prior knowledge, highlighting its significant advantages in jamming the subpulse-level frequency-agile radar.