[1]段海滨,马冠军,赵振宇.基于模糊规则和动态蚁群贝叶斯网络的无人作战飞机态势评估[J].智能系统学报,2013,8(02):119-127.[doi:10.3969/j.issn.1673-4785.201211031]
 DUAN Haibin,MA Guanjun,ZHAO Zhenyu.UCAV situation assessment based on fuzzy rules and dynamic ant colony-Bayesian network[J].CAAI Transactions on Intelligent Systems,2013,8(02):119-127.[doi:10.3969/j.issn.1673-4785.201211031]
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基于模糊规则和动态蚁群贝叶斯网络的无人作战飞机态势评估(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年02期
页码:
119-127
栏目:
出版日期:
2013-04-25

文章信息/Info

Title:
UCAV situation assessment based on fuzzy rules and dynamic ant colony-Bayesian network
文章编号:
1673-4785(2013)02-0119-09
作者:
段海滨1马冠军2赵振宇3
1. 北京航空航天大学 自动化科学与电气工程学院,北京 100191;
2. 北京航天自动控制研究所,北京 100854; 
3. 光电控制技术重点实验室, 河南 洛阳 471009
Author(s):
DUAN Haibin1 MA Guanjun2 ZHAO Zhenyu3
1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;
2. Beijing Aerospace Automatic Control Institute, Beijing 100854, China;
3. Science and Technology on Electro-Optic Control Laboratory, Luoyang 471009, China
关键词:
无人作战飞机态势评估模糊规则蚁群优化贝叶斯网络
Keywords:
unmanned combat aerial vehicle (UCAV) situation assessment fuzzy rules ant colony optimization Bayesian network
分类号:
TP18
DOI:
10.3969/j.issn.1673-4785.201211031
文献标志码:
A
摘要:
为解决无人作战飞机复杂环境下的态势评估难题,阐述了蚁群优化和贝叶斯网络基本原理和数学模型,设计了一种基于模糊规则和动态蚁群贝叶斯网络的无人作战飞机态势评估方法.该方法通过蚁群贝叶斯网络把不完备数据转换成完备数据,从而大大简化了学习的复杂度, 并保证算法能够向好的结构不断进化.利用模糊逻辑改进动态蚁群贝叶斯网络算法,引入基于模糊语言和规则的专家经验,结合单值评估结果与概率向量,评价了不同时刻无人作战飞机的行为能力等级,能够提高态势评估方法的智能性并应用于工程实际.通过仿真实验验证了该方法在解决复杂作战环境下无人作战飞机态势评估问题时的可行性和有效性.
Abstract:
In order to solve the challenging problem of unmanned combat aerial vehicles(UCAV) situation assessment in complex environments, based on the introduction of ant colony optimization, Bayesian network and mathematical model, a hybrid fuzzy rules and dynamic ant colony-Bayesian network was proposed in efforts to examine the situation assessment of UCAVs. The incomplete data was converted into a complete data packet by using a dynamic ant colony-Bayesian network, which can greatly simplify the complexity of learning, and ensure that the algorithm evolves into good structure. The dynamic ant colony-Bayesian network algorithm was improved by using fuzzy logic. The expert’s experience was adopted in the form of fuzzy language and rules. The single value assessment results were combined with the probability vector to evaluate the capacity level of UCAVs at different times, increase the intelligence of situation assessment, and practicality of engineering application. A series of experiments verified the feasibility and effectiveness of the proposed hybrid method for situation assessment of UCAVs in the complicated combat environment.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2012-11-19.
网络出版日期:2013-04-01. 
基金项目:国家自然科学基金资助项目(61273054,60975072); 航空科学基金资助项目(20115151019); 教育部新世纪优秀人才支持计划资助项目(NCET-10-0021). 
通信作者:段海滨.
E-mail:hbduan@buaa.edu.cn.
作者简介:
段海滨,男,1976年生,教授,博士生导师,博士,主要研究方向为无人机自主飞行控制与智能决策、计算机仿生视觉等.主持国家自然科学基金项目4项、国家“863”计划5项、航空科学基金3项、中组部青年拔尖人才支持计划、教育部新世纪优秀人才支持计划、北京市科技新星计划等课题.作为第一完成人获省部级科技成果一等奖、二等奖、三等奖各1项,发表学术论文60余篇,其中40余篇被SCI检索.
马冠军,男,1983年生,工程师,主要研究方向为无人飞行器自主飞行控制与智能决策、软件测试等.曾获国防科学技术进步三等奖,发表学术论文7篇.
赵振宇,男,1971年生,高级工程师,博士,主要研究方向为无人机任务决策与智能信息处理.曾获国防科学技术进步奖10余项,发表学术论文16篇.
更新日期/Last Update: 2013-05-26