An Intelligent Frequency Decision Method for a Frequency Agile Radar Based on Deep Reinforcement Learning

Abstract

The aiming jamming emitted by self-defense jammers renders various passive anti-jamming measures based on signal processing ineffective, posing severe threats to modern radars. Frequency agility, as an active countermeasure, enables the resistance of aiming jamming. In response to issues such as the unstable anti-jamming performance of traditional random frequency hopping, limited freedom in frequency selection, and the long time required for strategic learning, the paper proposes a fast-adaptive frequency-hopping strategy for a frequency agile radar. First, a frequency agile waveform with repeatable frequency selection is designed, providing more choices for an optimal solution. Accordingly, using the data collected through continuous confrontation between a radar and a jammer, and the exploration and feedback mechanism of deep reinforcement learning, a frequency-selection strategy is continuously optimized. Specifically, considering radar frequency from the previous time and jamming frequency perceived at the current time as reinforcement learning inputs, the neural network intelligently selects each subpulse frequency at the current time and optimizes the strategy until it is optimal based on the anti-jamming effectiveness evaluated by the target detection result and Signal-to-Jamming-plus-Noise Ratio (SJNR). To improve the convergence speed of the optimal strategy, the designed input state is independent of the historical time step, the introduced greedy strategy balances the search-utilization mechanism, and the SJNR differentiates rewards more. Multiple sets of simulations show that the proposed method can converge to the optimal strategy and has high convergence efficiency.

Keywords

• frequency agile radar
• anti-jamming
• waveform design
• aiming jamming
• deep q-network (dqn)