摘要
弹道导弹目标识别内容多样,症候识别是其中的一项重要内容,决定后续预警卫星发现导弹的概率和时间。提出了一种动态贝叶斯网络的弹道导弹发射症候识别方法,该方法首先选取4种发射症候特征,并给出每种特征的条件概率。其次建立症候识别贝叶斯网络模型,并设计了发射症候识别流程。最后通过仿真实验,验证了该模型比单一特征识别概率高,且具有一定的稳定性。研究表明该方法能够综合不同时刻的多种症候特征,给出科学合理的发射症候判读结果,具有一定的应用价值。
Identification of ballistic missiles contains many aspects, and the identification of launching symptom is an important part of it, which determines the subsequent detection probability of ballistic missile by early warning satellite and the warming time. In this paper, a synthetic method for identification of ballistic missile launching symptom is proposed based on Dynamic Bayesian Network （DBN）. Firstly, four kinds of symptom features are selected and the conditional probability of each feature is given. Then the DBN model of symptom identification is established and the identification process is designed. At last, it is verified by simulation that the model is much better and more robust than the method using single feature. The study shows that the method can fuse multiple kinds of features at different time, and give a scientific and reasonable symptom identification result.
出处
《电光与控制》
北大核心
2016年第11期9-12,共4页
Electronics Optics & Control
基金
电子对抗与信息控制国防科技重点实验室基金
国家自然科学基金青年项目(61401503
61602506)
中国博士后基金(20110491889)
全军军事类研究生资助课题(2014JY545)
学院创新基金(2013ZDJC0101
2014QNCX0115)
关键词
弹道导弹
发射症候
识别
动态贝叶斯网络
ballistic missile
launching symptom
identification
dynamic Bayesian network
