This study deals with the autonomous evasive maneuver strategy of unmanned combat air vehicle (UCAV), which is threatened by a high-performance beyond-visual-range (BVR) air-to-air missile (AAM). Considering tactical demands of achieving self-conflicting evasive objectives in actual air combat, including higher miss distance, less energy consumption and longer guidance support time, the evasive maneuver problem in BVR air combat is defined and reformulated into a multi-objective optimization problem. Effective maneuvers of UCAV used in different evasion phases are modeled in three-dimensional space. Then the three-level decision space structure is established according to qualitative evasive tactical planning. A hierarchical multi-objective evolutionary algorithm (HMOEA) is designed to find the approximate Pareto-optimal solutions of the problem. The approach combines qualitative tactical experience and quantitative maneuver decision optimization method effectively. Simulations are used to demonstrate the feasibility and effectiveness of the approach. The results show that the obtained set of decision variables constitutes nondominated solutions, which can meet different evasive tactical requirements of UCAV while ensuring successful evasion.

中文翻译：

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基于层次多目标进化算法的UCAV超视距空战规避机动策略


本研究涉及无人作战飞行器（UCAV）的自主规避机动策略，该策略受到高性能超视距（BVR）空对空导弹（AAM）的威胁。考虑到实际空战中实现自我冲突的规避目标的战术需求，包括更高的脱靶距离、更少的能量消耗和更长的制导支持时间，定义了超视距空战中的规避机动问题，并将其重新表述为多目标优化问题。在三维空间中对 UCAV 在不同躲避阶段的有效机动进行了建模。然后根据定性规避战术规划建立三级决策空间结构。分层多目标进化算法（HMOEA）旨在找到问题的近似帕累托最优解。该方法有效地结合了定性的战术经验和定量的机动决策优化方法。仿真用于证明该方法的可行性和有效性。结果表明，得到的决策变量集构成了非支配解，能够满足无人作战飞机不同的规避战术要求，同时保证规避成功。