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基于Mahalanobis距离的多飞行器协同组网效能评估

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分层网络通信是多飞行器协同的关键所在,其效能评估结果对于体系优化决策至关重要.为解决传统效能评估方法忽略分系统效能散布、以欧氏距离平均值为参照带来的缺陷,提出基于Mahalanobis距离下的多飞行器分层网络效能评估体系模型.在构建出无人飞行器通信网络效能评估体系的基础上,利用Mahalanobis距离充分表征通信网络体系内各节点效能的随机分布情况.将Mahalanobis距离建模下的效能值作为BP神经网络的输入,对于分层协同组网效能进行建模,结合粒子群算法(Particle Swarm Optimization,PSO)优化BP神经网络进行求解,充分考虑分层网络的异构信息,对于评估复杂条件下的多飞行器通信效能有重要的意义.以多飞行器通信为例,建立基于Mahalanobis距离的网络效能评估体系,对于分层网络的效能进行评估.评估结果用于优化各节点的效能指标、分布情况以及可靠度,验证了方法的有效性,实现多飞行器网络通信效能的精准提升.
Effectiveness evaluation of multi-aircraft collaborative networking based on Mahalanobis distance
Hierarchical network communication is essential for multi-aircraft collaboration,and perfor-mance evaluation is critical for making optimization decisions.To address the limitations of traditional perfor-mance evaluation methods,which often overlook the variance in subsystem performance and use the average Euclidean distance as a benchmark,a novel multi-aircraft hierarchical network performance evaluation system grounded in Mahalanobis distance is introduced.Building on the establishment of an unmanned aerial vehicle(UAV)communication network performance evaluation system,Mahalanobis distance is leveraged to compre-hensively capture the random distribution of each node's performance within the communication network sys-tem.The proposed approach is instrumental in assessing the communication performance of multiple aircraft un-der complex scenarios by using the Mahalanobis distance performance value as the input for a backpropagation(BP)neural network.The BP neural network is further enhanced by incorporating a particle swarm optimization(PSO)algorithm,which considers the heterogeneous information within the hierarchical network.A Mahalanobis distance based network performance evaluation system is developed to assess the hierarchical network's perfor-mance.The results are employed to optimize the performance indices,distribution,and reliability of each node,thereby validating the effectiveness of the proposed method and achieving accurate enhancement of the communi-cation efficiency of the multi-aircraft network.

hierarchical networkcommunication effectivenessassessment systemMahalanobis distanceparticle swarm optimizationunmanned aerial vehiclescollaborative networkingneural net-works

张振宁、王扬、林涛、魏佳宁、张克

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航天科工智能运筹与信息安全研究院有限责任公司,北京 100074

常州大学,常州 213164

分层网络 通信效能 评估体系 Mahalanobis距离 粒子群算法 无人飞行器 协同组网 神经网络

2024

战术导弹技术
中国航天科工飞航技术研究院(中国航天科工集团第三研究院)

战术导弹技术

CSTPCD北大核心
影响因子:0.304
ISSN:1009-1300
年,卷(期):2024.(2)
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