Evasive Maneuver Strategy for UCAV in Beyond-Visual-Range Air Combat Based on Hierarchical Multi-Objective Evolutionary Algorithm

• Main Authors: Zhen Yang, Deyun Zhou, Haiyin Piao, Kai Zhang, Weiren Kong, Qian Pan
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: BVR air combat; evasive maneuver; hierarchical evolutionary algorithm; multi-objective optimization; UCAV
• Online Access: https://ieeexplore.ieee.org/document/9026933/

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.