VAM-Enhanced Deep Reinforcement Learning for Cooperative Jamming Task Allocation

This paper addresses the cooperative jamming task allocation problem for multiple jammers against multiple communication targets in dynamic electronic warfare environments. Traditional algorithms struggle with adaptability and slow decision-making. To overcome these limitations, we propose a deep reinforcement learning (DRL) method enhanced by an improved Vogel’s approximation method (VAM) pre-training strategy, where VAM incorporates situational matrices for initial allocation. The proposed approach aims to maximize the total jamming situational value by intelligently assigning optimal target combinations to each multi-beam jammer. Specifically, the model evaluates the situational value of each target by integrating factors including the distance, target firepower, and threat levels, while adhering to system constraints of both jamming and target platforms. To meet the real-time decision-making requirements in dynamic adversarial environments, we integrate VAM with the proximal policy optimization (PPO) algorithm, leveraging human knowledge to accelerate the training process of DRL. Simulation results demonstrate that the proposed algorithm improves both the training efficiency and decision-making timeliness of the jamming allocation model, achieving cumulative reward increases of 38.45% and 13.86% over the respective baselines, while ensuring target coverage and effectively avoiding redundant or excessive jamming.