摘要
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。
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 comprehensively capture the random distribution of each node's performance within the communication network system.The proposed approach is instrumental in assessing the communication performance of multiple aircraft under 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 performance.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 communication efficiency of the multi-aircraft network.
作者
张振宁
王扬
林涛
魏佳宁
张克
Zhang Zhenning;Wang Yang;Lin Tao;Wei Jianing;Zhang Ke(CASIC Research Institute of Intelligent Decision Engineering,Beijing 100074,China;Changzhou University,Changzhou 213164,China)
出处
《战术导弹技术》
北大核心
2024年第2期62-70,134,共10页
Tactical Missile Technology
关键词
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
hierarchical network
communication effectiveness
assessment system
Mahalanobis distance
particle swarm optimization
unmanned aerial vehicles
collaborative networking
neural net-works
