Multifunctional Radar Cognitive Jamming Decision Based on Dueling Double Deep Q-Network

Abstract

To solve the inefficient and imprecise problem using the Deep Q-network (DQN) algorithm for the radar jamming decision, this paper proposes a multifunctional radar jamming decision optimization method based on the Dueling Double Deep Q-network (D3QN). First, we use a value function reflecting the radar state’s change, and an advantage function related to radar state S and jamming action A to improve the cognitive jamming level for unknown radar modes. Then, using the dueling networks for jamming strategy selection and effectiveness evaluation can further improve decision accuracy. Finally, we propose a prioritized experience replay mechanism during network training to shorten the decision-making time. The experimental results show that our proposed method completes decision tasks 2.1 times more efficiently than the DQN and improves decision accuracy by approximately 10% over DQN.

Keywords

• Dueling double deep q-network (D3QN)
• prioritized experience replay
• jamming decision-making
• reinforcement learning