基于自组织聚类的多机协同编批方法  

Multi-aircraft collaborative batching method based on self-organizing clustering

作　　者：张世辉 金同清 张运杰 周锐[3] 冉华明 周礼亮 ZHANG Shihui;JIN Tongqing;ZHANG Yunjie;ZHOU Rui;RAN Huaming;ZHOU Liliang(Shenyang Aircraft Design and Research Institute of AVIC,Shenyang 110035,China;China Academy of Aerospace Science and Innovation,Beijing 100176,China;School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China;CETC Key Laboratory of Avionic Information System Technology,Chengdu 610036,China)

机构地区：[1]中国航空工业集团公司沈阳飞机设计研究所,沈阳110035 [2]中国航天科技创新研究院,北京100176 [3]北京航空航天大学自动化科学与电气工程学院,北京100191 [4]中国电子科技集团公司航空电子信息系统技术重点实验室,成都610036

出　　处：《工程科学学报》2024年第7期1269-1278,共10页Chinese Journal of Engineering

基　　金：国家自然科学基金资助项目(61773031)。

摘　　要：针对多机协同对抗过程中的编批问题,设计了一种基于改进自组织迭代聚类的多机协同编批方法.该方法解决了传统自组织迭代聚类算法中人工参数设置不便利不直观的问题,能够在给定少数直观超参数条件下,使多机自主调整聚类过程中所涉及的参数,最终迭代出合理的编批结果.首先对高维多机态势信息进行标准化和主成分分析处理,从而确认新的向量空间;然后引入密度聚类中的邻域密度判别思想对传统自组织迭代聚类方法的合并和分裂操作进行改进,优化并减少了传统方法进行分裂和合并操作所涉及的人工参数,提升了执行编批聚类任务的智能自主性;最后选取算法评价指标,使用所提算法以及传统算法对多个人工合成数据以及实际想定场景进行聚类测试并对测试结果进行评价.人工合成数据仿真表明改进自组织迭代聚类算法在优化聚类过程中的人工参数后仍与原始算法表现出相当的性能,实际想定场景的编批结果进一步说明了改进自组织迭代聚类算法在具体应用场景中的有效性以及在未来实际场景中的实用性.This article addresses the bathing problem in multi-machine collaborative operations,proposing a method based on improved self-organizing iterative clustering.This approach circumvents the issues of traditional manual parameter setting in the self-organizing iterative clustering algorithm that is often inconvenient and non-intuitive.The proposed method allows multiple machines to autonomously adjust the parameters involved in the clustering process,given a small number of intuitive hyperparameters.The ultimate goal is to iterate toward reasonable editing results.Initially,this article focuses on selecting feature vectors for the multi-machine collaborative confrontation situation.It applies standardization and principal component analysis to high-dimensional multi-machine situation information to confirm the new vector space.This space mainly encompasses position information in three dimensions and speed information.Subsequently,the paper introduces the concept of neighborhood density discrimination from density clustering.This improves the merging and splitting operations of the traditional self-organizing iterative clustering method.It optimizes and reduces the artificial parameters involved in these operations,enhancing the intelligent autonomy for batch clustering tasks.Before optimization,artificial parameters primarily include the number of expected clusters,minimum number of points within a class,number of iterations,upper limit of standard deviation that limits data distribution within a class,and an allowable shortest distance indicator between classes.Post optimization,the artificial parameters are limited to the expected cluster quantity,minimum number of points,and the number of iterations within a single classification.These optimized parameters are relatively intuitive,and the algorithm output does not strongly correlate with the input parameters.Ultimately,the paper selects algorithm evaluation indicators,including Dunn,Davies–Bouldin,silhouette coefficient,and Calinski–Harabasz.It uses these t

关 键 词：多机协同编批 高维态势信息 自组织 聚类 超参数 

分 类 号：V221.3[航空宇航科学与技术—飞行器设计] TB553[理学—物理]