基于改进共生生物搜索算法的空战机动决策

高阳阳; 余敏建; 韩其松; 董肖杰

doi:10.13700/j.bh.1001-5965.2018.0395

基于改进共生生物搜索算法的空战机动决策

doi: 10.13700/j.bh.1001-5965.2018.0395

高阳阳^{1, 2},
余敏建^3, ,,
韩其松³,
董肖杰¹

1.
空军工程大学研究生院, 西安 710051
2.
中国人民解放军 93175部队, 长春 130000
3.
空军工程大学空管领航学院, 西安 710051

基金项目:

装备科研项目 2017024113B41057

详细信息

作者简介:
高阳阳  男, 硕士研究生。主要研究方向:航空兵指挥自动化

余敏建  男, 硕士, 教授。主要研究方向:航空兵指挥自动化

韩其松  男, 硕士, 讲师。主要研究方向:航空兵作战筹划

董肖杰  男, 硕士研究生。主要研究方向:航空兵作战筹划

通讯作者:
余敏建, E-mail:1090389519@qq.com

中图分类号: V271.4
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- 文章访问数: 749
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- 被引次数: 0
出版历程
- 收稿日期: 2018-06-27
- 录用日期: 2018-09-19
- 网络出版日期: 2019-03-20

Air combat maneuver decision-making based on improved symbiotic organisms search algorithm

1.
Graduate College, Air Force Engineering University, Xi'an 710051, China
2.
The Chinese People's Liberation Army, Unit 93175, Changchun 130000, China
3.
Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China

Funds:

Equipment Research Project 2017024113B41057

More Information

Corresponding author: YU Minjian, E-mail: 1090389519@qq.com

摘要

摘要:
针对现代空战机动决策问题，提出了一种基于改进共生生物搜索（SOS）算法的空战机动决策方法。首先，分析了传统基本机动动作库存在的不足，对其进行了改进和扩充，设计了11种常用的基本机动动作；然后，综合考虑角度、距离、速度、高度和战机性能优势，构造了战机机动决策优势函数；最后，针对传统共生生物搜索算法在收敛速度、收敛精度以及局部最优上存在的缺陷，将轮盘赌选择方法、动态变异率和梯度思想引入到传统算法当中，对算法有效性和算法性能进行了仿真分析。仿真结果表明，改进的共生生物搜索算法在收敛速度、收敛精度以及跳出局部最优上更具优势，能够满足空战机动决策需求。
- 机动决策 /
- 共生生物搜索(SOS) /
- 机动动作库 /
- 轮盘赌 /
- 动态变异 /
- 梯度
Abstract:
Aimed at the problem of modern air combat maneuver decision-making, an air combat maneuver decision-making method based on improved symbiotic organisms search (SOS) algorithm is proposed. Firstly, the shortcomings of the traditional basic maneuver inventory are analyzed, improved and expanded, and 11 kinds of common basic maneuver are designed. Secondly, considering the angle, distance, speed, altitude and the performance advantages of fighter planes, the decision-making advantage function of fighter planes is constructed. Finally, aimed at the shortcomings of the traditional SOS algorithm in convergence speed, convergence accuracy and local optimality, the roulette wheel selection method, dynamic variation rate and gradient idea are introduced into the traditional algorithm, and the effectiveness and performance of the algorithm are simulated and analyzed. The simulation results show that the improved SOS algorithm has more advantages in convergence speed, convergence accuracy and jump out of local optimum, and can meet the air combat maneuver decision-making requirements.
- maneuver decision-making /
- symbiotic organisms search (SOS) /
- maneuver inventory /
- roulette wheel /
- dynamic variation /
- gradient

HTML全文

图 1 基本机动动作库

Figure 1. Basic maneuver inventory

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图 2 空战优势评价指标体系

Figure 2. Air combat superiority evaluation index system

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图 3 敌我机几何态势划分示意图

Figure 3. Schematic of geometric situation division of enemy and our fighter

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图 4 轮盘赌操作示意图

Figure 4. Schematic of roulette wheel

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图 5 改进共生生物搜索算法流程

Figure 5. Flowchart of improved SOS algorithm

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图 6 敌我双方空战对抗三维航迹展示

Figure 6. Three-dimensional track display of both sides in air combat

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图 7 敌我双方航向角、飞行高度、飞行速度和空战优势变化曲线

Figure 7. Heading angle, height, speed and air combat superiority change curves of both sides

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图 8 3种算法收敛精度对比

Figure 8. Comparison of convergence accuracy of three algorithms

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图 9 3种算法最优值和消耗时间对比

Figure 9. Comparison of optimal values and time consumption of three algorithms

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