区间不确定性下的空中作战行动过程优选方法

doi:10.7527/S1000-6893.2020.24282

电子电气工程与控制

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区间不确定性下的空中作战行动过程优选方法

钟赟^1,2, 万路军^3,4, 张杰勇²

1. 中国人民解放军94040部队, 库尔勒 841000;
2. 空军工程大学信息与导航学院, 西安 710077;
3. 空军工程大学空管领航学院, 西安 710051;
4. 中国电科28所空中交通管理系统与技术国家重点实验室, 南京 210007

收稿日期:2020-05-25 修回日期:2020-07-20 发布日期:2020-09-04
通讯作者: 万路军 E-mail:pandawlj@126.com
基金资助:
国家自然科学基金（61703425）

Optimized selection method for air combat course of action with interval uncertainty

ZHONG Yun^1,2, WAN Lujun^3,4, ZHANG Jieyong²

1. Unit 94040 of PLA, Korla 841000, China;
2. Information and Navigation College, Air Force Engineering University, Xi'an 710077, China;
3. Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an 710051, China;
4. State Key Laboratory of Air Traffic Management Systems and Technologies, CETC 28, Nanjing 210007, China

Received:2020-05-25 Revised:2020-07-20 Published:2020-09-04
Supported by:
National Natural Science Foundation of China (61703425)

摘要/Abstract

摘要： 针对空中作战行动过程（COA）设计问题，根据动态影响网（DINs）理论和改进快速非支配排序遗传（NSGA-Ⅱ）算法，提出一种基于DINs和区间多目标优化的空中作战过程优选方法。首先，分析空中作战过程基本概念，分别进行静态和动态建模，并对参数不确定性进行分析。然后，基于改进Kendall协和系数检验法确定一致性检验后的关键参数，设计DINs概率传播算法。随后，分析期望效果实现概率与各关键参数的相关关系，在分析行动过程优选效果评价指标基础上，采用改进NSGA-Ⅱ算法对模型进行求解。最后，通过多组仿真案例，验证了模型的合理性，以及算法的有效性和优越性。

关键词: 行动过程, 动态影响网, 改进快速非支配排序遗传算法, 参数不确定性, Kendall协和系数检验法

Abstract: This paper proposes an optimized selection method for the design of air combat Course of Action (COA) based on Dynamic Influence Nets (DINs) and interval multi-objective optimization according to the theory of DINs and the improved Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ). We first analyzed the basic concepts of air combat COA, established the static and dynamic models separately, and performed a detailed analysis of parameter uncertainty. Then, based on the improved Kendall concordance test method, we determined the key parameters after the consistency test, and designed the DINs probability propagation algorithm. Subsequently, the correlation between the realization probability of desired effects and each key parameter was analyzed, and the improved NSGA-Ⅱ algorithm was used to solve the model after the analysis of the effect evaluation index of COA optimization. Finally, through multiple sets of simulation cases, the rationality of the model and the effectiveness and superiority of the algorithm were verified.

Key words: Course of Action (COA), Dynamic Influence Nets (DINs), improved Non-dominated Sorting Genetic Algorithm II (NSGA-Ⅱ), parameter uncertainty, Kendall concordance test

中图分类号:

钟赟, 万路军, 张杰勇. 区间不确定性下的空中作战行动过程优选方法[J]. 航空学报, 2021, 42(2): 324282-324282.

ZHONG Yun, WAN Lujun, ZHANG Jieyong. Optimized selection method for air combat course of action with interval uncertainty[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(2): 324282-324282.

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E-mail：hkxb@buaa.edu.cn

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区间不确定性下的空中作战行动过程优选方法

Optimized selection method for air combat course of action with interval uncertainty

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