基于注意力增强DDPG的短波协同测向与定位方法OA

Shortwave localization in complex scenarios plays a crucial role in battlefield communica-tions.Existing deep-learning-based intelligent shortwave direction-finding methods mainly rely on manual annotation,and there is a scarcity of high-quality shortwave signal samples.To address the aforementioned challenges,a shortwave autonomous direction-finding and localization method is pro-posed,based on a space-time high-resolution time-frequency representation of shortwave signals and an attention-enhanced deep deterministic policy gradient(DDPG)algorithm.A reinforcement learning environment is first designed based on space-time high-resolution time-frequency representation,along with an agent possessing a hybrid action space.Then,a reward function is designed based on the esti-mated localization results,thereby establishing an observable Markov decision process.Subsequently,by enhancing deep reinforcement learning with an attention mechanism for multi-level non-fine evalua-tion,automatic direction finding and positioning are achieved.This design reduces the need for manual labeling and enables autonomous evolution through simultaneous work and learning,gradually improv-ing the intelligent direction-finding and localization capabilities for shortwave signals.Experimental results show that,comparable localization accuracy and parameter estimation performance are main-tained,while the direction-finding and localization time is improved by approximately 77.7%.