摘要
随着战争形式的不断演变和武器装备的更新换代,空中战场态势日益复杂,迅速准确地识别目标作战意图是战场态势评估的一项重要内容,可以为指挥员的决策提供辅助信息,有利于掌握战争主动权。首先介绍了目标意图识别的基本概念及相关模型,界定了目标意图和意图识别的定义概念,从作战指挥决策流程和信息融合流程2个方面确定了目标意图识别的地位和重要性;其次目标特征和意图空间分别作为意图识别的输入属性和识别框架,是意图识别的基础并对此进行了综述;然后综述规则和模板匹配、证据理论、贝叶斯网络、传统机器学习和神经网络等5类常见的目标意图识别方法,介绍了每种识别方法的基本机理和识别过程,总结了其优缺点;最后对比分析了5类目标作战意图识别方法的性能,并对其未来研究方向进行了展望。
With the continuous evolution of war forms and the upgrading of weapons and equipment,the air battlefield situation is becoming more and more complex.Quickly and accurately identifying the combat intention of the target is an important content of battlefield situation assessment,which can provide auxiliary information for commanders to make decisions and help them to seize the initiative in the war.This paper firstly introduces the basic concepts and related models of target intention recognition,defines the concepts of target intention and intention recognition,and determines the status and importance of target intention recognition from two aspects of the operational command decision-making process and information fusion process.Secondly,target features and intention space,as input attributes and recognition framework for intention recognition respectively,are the basis of intention recognition and are reviewed.Then,five common target intention recognition methods such as rule and template matching,evidence theory,Bayesian network,traditional machine learning and neural network are reviewed.The basic mechanism and recognition process of each recognition method are introduced,and its advantages and disadvantages are summarized.Finally,the performance of five kinds of target combat intention recognition methods is compared and analyzed,and the future research direction is prospected.
作者
张晨浩
周焰
蔡益朝
郭佳琦
ZHANG Chenhao;ZHOU Yan;CAI Yichao;GUO Jiaqi(Air Force Early Warning Academy,Wuhan 430014,China;PLA 93135 Troops,Guangzhou 510080,China)
出处
《现代防御技术》
北大核心
2024年第4期1-15,共15页
Modern Defence Technology
关键词
空中战场态势
目标作战意图
意图识别
规则和模板匹配
证据理论
贝叶斯网络
机器学习
神经网络
air battlefield situation
target operational intent
intention recognition
matching rules and templates
evidence theory
Bayesian network
machine learning
neural network
