聚类分析
计算机科学
遗传算法
弹道
战场
人工智能
实时计算
计算机视觉
算法
机器学习
古代史
物理
天文
历史
作者
Xiao Wang,Hanyang Chen,Tao Liu,Kai He,Di Ding,Enmi Yong
标识
DOI:10.1109/cac53003.2021.9727313
摘要
In this paper, an improved K-means clustering analysis algorithm is proposed, which is applied to the situation that there are too many reconnaissance targets in the battlefield environment. The purpose is to reduce the number of target points, so as to reduce the number of reconnaissance tasks of UAV, so that the UAV can achieve the same reconnaissance effect at a shorter distance. After the clustering analysis of target points, the traditional genetic algorithm is used to plan the trajectory for the UAV. According to the results, the reconnaissance effect before and after the improvement is compared. The results show that the improved method reduces the number of reconnaissance points and the length of the UAV’s trajectory, and achieves better optimization effect with less cost.
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