ALI-MAPPO: Attention on Local Information Aided MAPPO Algorithm for Power Allocation of Wireless Cognitive Jamming Systems

Intelligent decision-making is an essential subtask for wireless electromagnetic countermeasures, with the objective of imposing effective electronic jamming based on the observation and estimation of rival equipment. With the proliferation of radar networks, the effectiveness of a single jammer is being questioned, raising research interest in jamming resource allocation of cognitive jamming system. This article presents a study on the multiagent collaborative jamming power allocation task to achieve the overall barrage jamming of a radar network using limited jamming resources. This task is first formulated as a decentralized partially observable Markov decision process to be oriented toward the fully cooperative setting. And we introduce an improved centralized-training-with-decentralized-execution paradigm, namely, designing an “attention on local information”-aided multiagent proximal policy optimization (ALI-MAPPO) network. The core motivation of ALI is that the customized global information with weights is more conducive to learning effective individual value functions. Through adequate network training, the cognitive jamming system utilizes observation information and well-trained policies to make optimal decisions individually. In addition, we incorporate radar antijamming signal processing features, such as frequency agility and uncertainty in parameter estimation, into the scenario design. Extensive experimental results demonstrate that the ALI-MAPPO enables the cognitive jamming system to generate robust policies and obtain the best jamming performance compared to other existing multiagent reinforcement learning methods.