现代防御技术2025，Vol.53Issue(1)：52-62,11.DOI:10.3969/j.issn.1009-086x.2025.01.006
基于度量学习的半监督空中目标作战意图识别
Semi-supervised Air Targets Combat Intention Recognition Based on Metric Learning
张晨浩 1周焰 1梁复台 2周通 3宋子豪 1袁凯1
作者信息
- 1. 空军预警学院,湖北 武汉 430014
- 2. 空军预警学院,湖北 武汉 430014||中国人民解放军31121部队,江苏 南京 210000
- 3. 中国人民解放军93950部队,青海 海西 816000
- 折叠
摘要
Abstract
The air battlefield situation provides a general description of the actions and states of all participants,while target combat intention recognition serves as a critical foundation for air battlefield posture assessment.To address the challenge of acquiring a large amount of labeled air target battlefield data amidst intense confrontation and rapidly evolving scenarios,a semi-supervised air targets combat intention recognition model is proposed based on metric learning.The model offers a method for uncovering potential patterns from unlabeled samples,thus reducing the reliance on extensive labeled data.In this model,the target time-series data encoder reduces the dimensionality of target data and produces an embedded representation.Based on this,loss values are calculated by measuring the similarity between labeled target sequences and intention types,as well as between labeled and unlabeled target sequences.The experimental results demonstrate that the model achieves combat intention recognition accuracy rates of 86%,89%,and 91%with labeled sample rates of 30%,40%,and 50%,respectively.关键词
空中目标/战场态势/作战意图/意图识别/度量学习/半监督学习Key words
air targets/battlefield situation/combat intention/intention recognition/metric learning/semi-supervised learning分类
军事科技引用本文复制引用
张晨浩,周焰,梁复台,周通,宋子豪,袁凯..基于度量学习的半监督空中目标作战意图识别[J].现代防御技术,2025,53(1):52-62,11.
