现代防御技术 ›› 2022, Vol. 50 ›› Issue (3): 109-118.DOI: 10.3969/j.issn.1009-086x.2022.03.015

• 仿真技术 • 上一篇    下一篇

无人机双机空战对抗侧向机动路径规划

孙丰础, 甘旭升   

  1. 空军工程大学 空管领航学院,陕西 西安 710051
  • 收稿日期:2021-11-01 修回日期:2021-12-28 出版日期:2022-06-28 发布日期:2022-07-01
  • 作者简介:孙丰础(1999-),男,山东淄博人。管制员,本科,研究方向为无人机作战效能评估。
  • 基金资助:
    国家自然基金项目(52074309)

Lateral Maneuver Path Planning of Two UAV Formation Air Combat

Feng-chu SUN, Xu-sheng GAN   

  1. Air Force Engineering University,Air Traffic Control and Navigation College,Shaanxi Xi’an 710051,China
  • Received:2021-11-01 Revised:2021-12-28 Online:2022-06-28 Published:2022-07-01

摘要:

无人机自主决策能力的不足一直是制约无人机进入空战对抗领域的主要因素。在已有的大量研究中,主要是采用构建动作库和机器学习算法的方式实现无人机的空战机动决策。但对无人机训练的品质会极大影响机动决策的效果。使用启发式算法构建无人机侧向机动对抗的攻击路径可摆脱对训练库的依赖,且未来可拓展性强,便于优化。基于A-star算法,构建了一种适用于无人机中距空战侧向机动决策的路径规划算法,算法可进行位置和速度规划。通过对双机协同策略的制定,设计了无人机双机对抗中的目标分配算法,可将无人机的单机对抗拓展至双机对抗的情景。通过仿真试验,验证了算法在达成战术效果和决策效率方面的可行性。

关键词: 无人机, 空战对抗, A-star算法, 路径规划, 速度规划

Abstract:

The lack of autonomous decision-making ability of UAV has always been the main factor restricting UAV to enter the field of air combat. In a large number of existing research, action store combined with machine learning algorithm is mainly used to realize UAV air combat maneuver decision. However, the quality of UAV training will greatly affect the effect of maneuver decision. Using heuristic algorithm to construct the attack path of UAV lateral maneuver can get rid of the dependence on training method, and has strong expansibility in the future, which is easy to optimize. Based on the A-star algorithm, a path planning algorithm for UAV lateral maneuver decision-making in medium range air combat is proposed. The algorithm can plan position and speed of UAV. Through the formulation of the two aircraft cooperation strategy, the target allocation algorithm in the two UAV is designed, which can expand the UAV single aircraft confrontation to the scenario of two aircraft confrontation. Through the simulation experiment, the feasibility of the algorithm in achieving tactical effect and decision efficiency is verified.

Key words: unmanned aerial vehicl (UAV), air combat, A-star algorithm, path planning, speed planning

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