[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(2):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(2):119-127.[doi:10.3969/j.issn.1673-4785.201211031]
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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(2):119-127.[doi:10.3969/j.issn.1673-4785.201211031]
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
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第2期
页码:
119-127
栏目:
学术论文—智能系统
出版日期:
2013-04-25
- Title:
- UCAV situation assessment based on fuzzy rules and dynamic ant colony-Bayesian network
- 文章编号:
- 1673-4785(2013)02-0119-09
- Keywords:
- unmanned combat aerial vehicle (UCAV); situation assessment; fuzzy rules; ant colony optimization; Bayesian network
- 分类号:
- TP18
- 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:
-
［1］NIKOLOS I K, VALAVANIS K P, TSOURVELOUDIS N C, et al. Evolutionary algorithm based offline/online path planner for UAV navigation［J］. IEEE Transactions on Systems, Man, and Cybernetics—Part B：2003, 33(6): 898- 912.
［2］罗德林, 段海滨, 吴顺祥, 等. 基于启发式蚁群算法的协同多目标攻击空战决策研究［J］. 航空学报, 2006, 27(6): 1166-1170.
LUO Delin, DUAN Haibin, WU Shunxiang, et al. Research on air combat decision-making for cooperative multiple target attack using heuristic ant colony algorithm［J］. Acta Aeronautica et Astronautica Sinica, 2006, 27(6): 1166-1170.
［3］段海滨, 丁全心, 常俊杰, 等. 基于并行蚁群优化的多UCAV任务分配仿真平台［J］. 航空学报, 2008, 29(suppl.): 192-197.
?DUAN Haibin, DING Quanxin, CHANG Junjie, et al. Multi-UCAVs task assignment simulation platform based on parallel ant colony optimization［J］. Acta Aeronautica et Astronautica Sinica, 2008, 29(suppl.): 192-197.
［4］ACID S, De CAMPOS L M. A hybrid methodology for learning Bayesian networks: Benedict［J］. International Journal of Approximate Reasoning, 2001, 27 (3): 235-262.
［5］MENG G L, GONG G H. Algorithm of battlefield situation assessment based on Bayesian network and rough set theory［C］//Proceedings of the 2008 Asian Simulation Conferenc. Beijing, China, 2008: 776-779.
［6］JAN T. Neural network based threat assessment for automated visual surveillance［C］//IEEE International Joint Conference on Neural Networks. Sydney, Australia, 2004, 2: 1309-1312.
［7］HINMAN M L. Some computational approaches for situation assessment and impact assessment［C］//Proceedings of the 5th International Conference on Information Fusion. New York, USA, 2002: 687-693.
［8］GONSALVES P G, BURGE J E, HARPER K A. Architecture for genetic algorithm-based threat assessment［C］//Proceedings of the 6th International Conference on Information Fusion. New York, USA, 2003: 965-971.
［9］康长青，郭立红，罗艳春，等. 基于模糊贝叶斯网络的态势威胁评估模型［J］. 光电工程, 2008, 35(5): 2-5.
?KANG Changqing, GUO Lihong, LUO Yanchun, et al. Model of situation and threat assessment based on fuzzy Bayesian network［J］. Opto-electronic Engineering, 2008, 35(5): 2-5.
［10］DAS S, GREY R, GONSALVES P. Situation assessment via Bayesian belief networks［C］//Proceedings of the 5th International Conference on Information Fusion. Cambridge, USA, 2002: 664-671.
［11］PEDRO L, CINDY M H K, ROBERTO H M. Learning Bayesian network structures by searching for the best ordering with genetic algorithms［J］. IEEE Transactions on Systems, Man, and Cybernetics—Part A. 1996, 26(4): 487-494.
［12］COOPER G F. A Bayesian method for the induction of probabilistic networks from data［J］. Machine Learning, 1992, 9: 309-347.
［13］周志强，张晓燕. 基于贝叶斯网络的博弈融合态势评估方法［J］. 计算机与数字工程, 2008, 36(10): 17-19. ZHOU Zhiqiang, ZHANG Xiaoyan. Situation assessment of information fusion based on Bayesian networks［J］. Computer & Digital Engineering, 2008, 36(10): 17-19.
［14］DORIGO M, MANIEZZO V, COLORNI A. The ant system optimization by a colony of cooperating agents［J］. IEEE Transactions on Systems, Man, and Cybernetics—Part B： 1996, 26(1): 1-13.［15］BONABEAU E, DORIGO M, THERAULAZ G. Inspiration for optimization from social insect behavior［J］. Nature, 2000, 406(6): 39-42.
［16］段海滨, 张祥银, 徐春芳. 仿生智能计算［M］. 北京: 科学出版社, 2011： 12-15.
［17］LUIS M C, JUAN M F, JOSE A G, et al. Ant colony optimization for learning Bayesian networks［J］. International Journal of Approximate Reasoning， 2002, 31(3): 291-311.
［18］JI Junzhong，ZHANG Hongxun，HU Renbing，et al. A Bayesian network learning algorithm based on independence test and ant colony optimization［J］. Acta Automatica Sinica, 2009, 35(3): 281-288.
［19］PINTO P C, NAGELE A, DEJORI M, et al. Learning of Bayesian networks by a local discovery ant colony algorithm［C］// Proceedings of the 2008 IEEE World Congress on Computational Intelligence. Hong Kong, China, 2008: 2741-2748.
［20］De CAMPOS L M, HUETE J F. A new approach for learning belief networks using independence criteria［J］. International Journal of Approximate Reasoning, 2000, 24(1): 11-37.
［21］CHENG J, GREINER R, KELLY J, et al. Learning Bayesian networks from data: an information theory based approach［J］. Artificial Intelligence, 2002, 137(1/2): 43-90.
［22］GUARRO S, YAU M. On the nature and practical handling of the Bayesian aggregation anomaly［J］. Reliability Engineering and System Safety, 2009, 94(6): 1050-1056.
［23］LEE J Y, IM S M, LEE J J. Bayesian network-based non-parametric compact genetic algorithm［C］//Proceedings of the 6th IEEE International Conference on Industrial Informatics. Daejeon, Korea, 2008: 359-364.
［24］DUAN H B, SHAO S, SU B W, et al. New development thoughts on the bio-inspired intelligence based control for unmanned combat aerial vehicle［J］. Science China Technological Sciences, 2010, 53(8): 2025-2031.
［25］KLEIN G A, CALDERWOOD R, MACGREGOR D. Critical decision method for eliciting knowledge［J］. IEEE Transactions on Systems, Man and Cybernetics—part, 1989, 19(3): 462-472.
［26］REICHARD K M. Integrating self-health awareness in autonomous systems［J］. Robotics and Autonomous Systems, 2004, 49: 105-112.
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).
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、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