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autonomous maneuver decisions via Transfer Learning Pigeon-Inspired Optimization for UCAVs in Dogfight Engagements

IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1639-1657页

作者： Wanying Ruan Haibin Duan Yimin Deng the School of Automation Science and Electrical Engineering Beihang University(BUAA)Beijing 100083China the State Key Laboratory of Virtual Reality Technology and Systems the School of Automation Science and Electrical EngineeringBeihang University(BUAA)Beijing 100083 Peng Cheng Laboratory Shenzhen 518000China

This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight ***,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink ***,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be ***,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range ***,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by ***,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization ***,the convergence and time complexity of TLPIO are *** analysis with other classical optimization algorithms highlights the advantage of *** the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive *** effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.

关键词： autonomous maneuver decisions dogfight engagement game mixed strategy transfer learning pigeon-inspired optimization(TLPIO) unmanned combat aerial vehicle(UCAV)

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