考虑局部特征的无人机集群关键节点识别方法

doi:10.16182/j.issn1004731x.joss.24-FZ0790

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (12): 2824-2833.doi: 10.16182/j.issn1004731x.joss.24-FZ0790

• 论文 • 上一篇

考虑局部特征的无人机集群关键节点识别方法

石成泷¹, 华翔¹, 王东¹, 张金金², 蒋天启³, 党元章³

^1.西安工业大学机电工程学院，陕西西安 710021
^2.西安工业大学兵器科学与技术学院，陕西西安 710021
^3.西安工业大学电子信息工程学院，陕西西安 710021

收稿日期:2024-07-19 修回日期:2024-09-29 出版日期:2024-12-20 发布日期:2024-12-20
通讯作者: 华翔
第一作者简介:石成泷(1997-)，男，博士生，研究方向为无人机集群协同控制。
基金资助:
陕西省重点研发计划(2023-YBGY-227);陕西省自然科学基础研究计划(2023-JC-QN-0705);西安市科技计划(2022JH-RYFW-0138);碑林区科技计划(GX2216)

Critical Node Identification Method for Unmanned Aerial Vehicle Cluster Considering Localized Features

Shi Chenglong¹, Hua Xiang¹, Wang Dong¹, Zhang Jinjin², Jiang Tianqi³, Dang Yuanzhang³

^1.School of Mechatromic Engineering, Xi'an Technological University, Xi'an 710021, China
^2.School of Armament Science and Technology, Xi'an Technological University, Xi'an 710021, China
^3.School of Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China

Received:2024-07-19 Revised:2024-09-29 Online:2024-12-20 Published:2024-12-20
Contact: Hua Xiang

摘要/Abstract

摘要：

针对无人机集群关键节点识别方法注重网络全局，忽略节点与其局部特征之间关联性的问题，提出一种考虑局部特征的无人机集群关键节点识别方法。基于复杂网络理论构建无人机集群网络模型；引入拉普拉斯能量评估两跳范围内节点的重要程度，结合信息熵评估特定模体中节点的重要程度，以综合识别关键节点。结果表明：该方法识别的关键节点表现出较好的区分度，验证了其有效性，相比对比方法，在连续失效下抗毁性下降趋势显著，验证了其准确性。

关键词: 无人机集群, 关键节点, 局部特征, 拉普拉斯能量, 模体

Abstract:

Aiming at the problem that the UAV cluster critical node identification methods focus on the global network and ignore the correlation between nodes and their local features, a critical nodes identification method for unmanned aerial vehicle cluster considering local features is proposed. An unmanned aerial vehicle cluster network model is constructed based on complex network theory. The Laplacian energy is introduced to evaluate the importance of node within two hops, and information entropy is combined to evaluate the importance of node in a specific motif to comprehensive identify the critical nodes. Simulation results demonstrate that this method identifies critical nodes that perform well in terms of differentiation, validating its effectiveness. This method exhibits a significant improvement in resilience against continuous failures compared to the comparative methods, further confirming its superiority.

Key words: UAV cluster, critical nodes, local features, Laplacian energy, motif

中图分类号:

TP301

石成泷,华翔,王东等 . 考虑局部特征的无人机集群关键节点识别方法[J]. 系统仿真学报, 2024, 36(12): 2824-2833.

Shi Chenglong,Hua Xiang,Wang Dong,et al . Critical Node Identification Method for Unmanned Aerial Vehicle Cluster Considering Localized Features[J]. Journal of System Simulation, 2024, 36(12): 2824-2833.

图/表 9

图1

图2

图3

图4

图5

图6

表1

无人机集群关键节点区分度 (%)

算法	$n = 30$		$n = 50$
算法	$t 0$ 时刻	$t 1$ 时刻	$t 0$ 时刻	$t 1$ 时刻
DC	64.37	57.55	64.65	64.39
CC	94.56	97.91	98.37	96.92
EC	100	100	99.84	100
CNILF	97.71	98.17	97.40	99.35

表1

图7

图8

参考文献 16

1	Yang Yuanzhi, Wang Xing, Chen You, et al. Identifying Key Nodes in Complex Networks Based on Global Structure[J]. IEEE Access, 2020, 8: 32904-32913.
2	Ullah Aman, Wang Bin, Sheng Jinfang, et al. Identification of Nodes Influence Based on Global Structure Model in Complex Networks[J]. Scientific Reports, 2021, 11(1): 6173.
3	李亚飞, 梁琛. 基于TOPSIS融合法的航路网关键节点识别及抗毁性研究[J]. 飞行力学, 2022, 40(6): 83-87.
	Li Yafei, Liang Chen. Research on Key Node Identification and Damage Resistance of Airway Network Based on TOPSIS Fusion Method[J]. Flight Dynamics, 2022, 40(6): 83-87.
4	Lei Mingli, Kang Hao Cheong. Node Influence Ranking in Complex Networks: A Local Structure Entropy Approach[J]. Chaos Solitons & Fractals, 2022, 160: 112136.
5	梁威, 孙鹏, 张杰勇, 等. 基于二项熵和邻域节点间范德华力的关键节点识别方法[J]. 空军工程大学学报, 2024, 25(4): 72-78.
	Liang Wei, Sun Peng, Zhang Jieyong, et al. A Key Node Identification Method Based on Binomial Entropy and van der Waals Forces Between Neighboring Nodes[J]. Journal of Air Force Engineering University, 2024, 25(4): 72-78.
6	孙利娜, 梁葆华, 陈志伟. 综合节点重要性及攻击代价的作战网络抗毁性分析[J]. 火力与指挥控制, 2022, 47(10): 119-124.
	Sun Lina, Liang Baohua, Chen Zhiwei. Combat Network Invulnerability Analysis Considering Node Importance and Attack Cost[J]. Fire Control & Command Control, 2022, 47(10): 119-124.
7	Wan Yu, Tang Jun, Lao Songyang. Distributed Conflict-detection and Resolution Algorithms for Multiple UAVs Based on Key-node Selection and Strategy Coordination[J]. IEEE Access, 2019, 7: 42846-42858.
8	Teng Min, Gao Chao, Wang Zhen, et al. A Communication-based Identification of Critical Drones in Malicious Drone Swarm Networks[J]. Complex & Intelligent Systems, 2024, 10(3): 3197-3211.
9	李杰, 王瑛, 孙贇, 等. 基于关键节点识别的集群无人机通信网络抗毁性研究[C]//2020中国自动化大会(CAC2020)论文集. 上海: 中国自动化学会, 2020: 570-575.
10	顾凌枫, 何明, 陈国友, 等. 无人机集群系统弹性研究[J]. 系统工程与电子技术, 2021, 43(1): 156-162.
	Gu Lingfeng, He Ming, Chen Guoyou, et al. Research on Unmanned Aerial Vehicle Swarm System Resilience[J]. Systems Engineering and Electronics, 2021, 43(1): 156-162.
11	Zhao Shuying, Sun Shaowei. Identification of Node Centrality Based on Laplacian Energy of Networks[J]. Physica A: Statistical Mechanics and Its Applications, 2023, 609: 128353.
12	徐越, 刘雪明. 基于三元闭包模体的关键节点识别方法[J]. 复杂系统与复杂性科学, 2023, 20(4): 33-39.
	Xu Yue, Liu Xueming. Method for Identifying Critical Nodes Based on Closed Triangle Motifs[J]. Complex Systems and Complexity Science, 2023, 20(4): 33-39.
13	吴亚丽, 任远光, 董昂, 等. 基于邻域K-shell分布的关键节点识别方法[J]. 计算机工程与应用, 2024, 60(2): 87-95.
	Wu Yali, Ren Yuanguang, Dong Ang, et al. Key Nodes Identification Method Based on Neighborhood K-shell Distribution[J]. Computer Engineering and Applications, 2024, 60(2): 87-95.
14	Latora Vito, Marchiori Massimo. Efficient Behavior of Small-world Networks[J]. Physical Review Letters, 2001, 87(19): 198701.
15	赵娜, 柴焰明, 尹春林, 等. 基于最大连通子图相对效能的相依网络鲁棒性分析[J]. 电子科技大学学报, 2021, 50(4): 627-633.
	Zhao Na, Chai Yanming, Yin Chunlin, et al. Robustness Analysis of Interdependent Networks Based on the Largest-component Relative Efficiency[J]. Journal of University of Electronic Science and Technology of China, 2021, 50(4): 627-633.
16	贺正求, 刘海业, 杨小军, 等. 基于自然连通度的指挥信息系统结构网络化效能分析[J]. 指挥控制与仿真, 2023, 45(1): 51-55.
	He Zhengqiu, Liu Haiye, Yang Xiaojun, et al. Analysis of Networked Effectiveness for Command Information System Structure Based on Natural Connectivity[J]. Command Control & Simulation, 2023, 45(1): 51-55.

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考虑局部特征的无人机集群关键节点识别方法

Critical Node Identification Method for Unmanned Aerial Vehicle Cluster Considering Localized Features

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