Reinforcement Learning-Based Antijamming Strategy for Self-Defense Jammer-Aided Radar Systems

Abstract

The increasingly intelligence of electronic warfare jamming techniques and the threat of main-lobe jamming have significantly deteriorated radar detection capabilities. As a result, it is essential for radar systems to implement active and advanced countermeasures to effectively combat such interference. Current radar active countermeasures, although effective in anti-jamming, often compromise radar operations by consuming valuable and limited resources of radar. Inspired by the use of electronic warfare pods to evade radar detection, a collaborative framework is designed between radar and radar self-defense jammer (RSDJ) for active defense. This article proposes RSDJ-aided strategies to enhance radar antijamming capability and survivability, while significantly reducing the demand on radar's operational resources. Considering the practical limitations on available frequency resources, we devise dual coordination strategies that optimize the collaboration between the radar and RSDJ. The coordination strategies unitize different frequency resources constrained by the requirement of mutual interference reduction. Confronting the unpredictability of adversary jamming in practice, this work employs reinforcement learning (RL) to develop coordination strategies without the need for pretraining or prior information. The application of RL enables the RSDJ to effectively deceive the adversary jammer and enhance radar antijamming capabilities in unknown environments. Comparative simulations demonstrate the effectiveness of proposed strategies, showing a notable improvement in radar anti-jamming performance and interference mitigation of nearly 50%. This advancement significantly boosts the adaption of radar operations amidst complex electronic warfare environments.