基于回归型支持向量机的空战目标威胁评估

郭 辉; 徐浩军; 刘 凌

基于回归型支持向量机的空战目标威胁评估

空军工程大学工程学院, 西安 710038

基金项目: 国家自然科学基金资助项目(70671106)

详细信息

作者简介:
郭辉(1981-),男,吉林柳河人,博士生,guohui731506@163.com.

中图分类号: V 271
计量
- 文章访问数: 3392
- HTML全文浏览量: 200
- PDF下载量: 1096
- 被引次数: 0
出版历程
- 收稿日期: 2009-06-19
- 网络出版日期: 2010-01-31

Target threat assessment of air combat based on support vector machines for regression

Engineering Institute, Air Force Engineering University, Xi-an 710038, China

摘要

摘要: 空战目标威胁评估是协同多目标攻击中的关键问题.针对传统空战目标威胁评估方法在确定权重系数方面的不足,提出了一种新的基于回归型支持向量机的评估方法.在分析了现有的空战目标威胁评估方法中距离威胁模型存在缺陷的基础上,提出了改进的距离威胁模型.建立了基于回归型支持向量机的空战目标威胁评估模型,利用该模型对想定的空战目标进行了威胁评估.仿真结果表明,该方法具有很好的预测能力,可以快速、准确地完成空战目标威胁评估.
- 空战 /
- 威胁评估 /
- 支持向量机 /
- 回归型支持向量机
Abstract: Target threat assessment method for air combat was a key problem of cooperative multiple target attack. Aiming at the deficiencies of traditional target threat assessment method for air combat in calculating the weights, a new assessment method based on support vector machines for regression was proposed. An improved distance threat model was proposed based on analyzing the existing target threat assessment methods for air combat which had disadvantages in distance threat model. The model of target threat assessment for air combat was established based on support vector machines for regression, then using this model the threat of some supposed air combat target was evaluated . The simulation results show that the method has good ability of forecast, which can accomplish target threat assessment for air combat fleetly and exactly.
- air combat /
- threat assessment /
- support vector machines /
- support vector machines for regression

HTML全文

参考文献(1)

[1] 王俊,姜长生.基于LSRBF神经网络的空战目标威胁评估[J].电光与控制,2007,14(4):43-48 Wang Jun,Jiang Changsheng.Target threat assessment based on LSRBF neural network for air combat[J].Electronics Optics & Control,2007,14(4):43-48(in Chinese) [2] 邱浪波,刘作良,刘明.一种应用神经网络技术的威胁估计算法[J].空军工程大学学报:自然科学版,2002,3(6):25-28 Qiu Langbo,Liu Zuoliang,Liu Ming.A threat assessment algorithm by using the neural network techniques[J].Journal of Air Force Engineering University:Natural Science Edition,2002,3(6):25-28(in Chinese) [3] 王向华,覃征,刘宇,等.径向基神经网络解决威胁排序问题[J].系统仿真学报,2004,16(7):1576-1579 Wang Xianghua,Qin Zheng,Liu Yu,et al.RBF neural network for threat sequencing[J].Journal of System Simulation,2004,16(7):1576-1579(in Chinese) [4] 高尚,杨静宇.基于支持向量机的威胁判断模型[J].火力与指挥控制,2006,31(2):55-59 Gao Shang,Yang Jingyu.Threat estimation model based on support vector machines[J].Fire Control and Command Control,2006,31(2):55-59(in Chinese) [5] 高尚.基于主成分分析和支持向量机的威胁判断模型[J].数学的实践与认识,2008,38(4):1-6 Gao Shang.Threat estimation model based on principal component analysis and support vector machines[J].Mathematics in Practice and Theory,2008,38(4):1-6(in Chinese) [6] 董彦非,郭基联,张恒喜.多机空战目标威胁评估算法[J].火力与指挥控制,2002,27(4):73-76 Dong Yanfei,Guo Jilian,Zhang Hengxi.Threat assessment for multi-aircraft air combat[J].Fire Control and Command Control,2002,27(4):73-76(in Chinese) [7] 谢希权,李伟仁.单机多目标攻击逻辑的对策型决策[J].系统工程与电子技术,2000,22(7):28-31 Xie Xiquan,Li Weiren.Game form decision for air combat logic of multi-target attack system[J].Systems Engineering and Electronics,2000,22(7):28-31(in Chinese) [8] 朱宝鎏,朱荣昌,熊笑非.作战飞机效能评估[M].北京:航空工业出版社,2006:63-68 Zhu Baoliu,Zhu Rongchang,Xiong Xiaofei.Effectiveness evaluation of combat aircraft[M].Beijing:Aeronautics Industry Press,2006:63-68(in Chinese) [9] 胡良谋,曹克强,徐浩军.基于回归型支持向量机的液压舵机故障诊断[J].系统仿真学报,2007,19(23):5509-5512 Hu Liangmou,Cao Keqiang,Xu Haojun.Fault diagnosis for hydraulic actuator based on support vector regression[J].Journal of System Simulation,2007,19(23):5509-5512(in Chinese)

施引文献

资源附件(0)

访问统计