Unit coordination knowledge enhanced autonomous decision-making approach of heterogeneous UAV formation  

作　　者：Yuqian WU Haoran ZHOU Ling PENG Tao YANG Miao WANG Guoqing WANG 

机构地区：[1]School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China [2]College of Automation,Northwestern Polytechnical University,Xian 710072,China [3]China Ship Development and Design Center,Wuhan 430064,China

出　　处：《Chinese Journal of Aeronautics》2025年第2期381-402,共22页中国航空学报(英文版)

摘　　要：Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.

关 键 词：Unmanned aerial vehicle Autonomous decision making Autonomous agents Data mining Knowledge mining Reinforcement learning 

分 类 号：G63[文化科学—教育学]