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基于空域划分的超视距空战态势威胁评估

肖亮 黄俊 徐钟书

肖亮, 黄俊, 徐钟书等 . 基于空域划分的超视距空战态势威胁评估[J]. 北京航空航天大学学报, 2013, 39(10): 1309-1313.
引用本文: 肖亮, 黄俊, 徐钟书等 . 基于空域划分的超视距空战态势威胁评估[J]. 北京航空航天大学学报, 2013, 39(10): 1309-1313.
Xiao Liang, Huang Jun, Xu Zhongshuet al. Modeling air combat situation assessment based on combat area division[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(10): 1309-1313. (in Chinese)
Citation: Xiao Liang, Huang Jun, Xu Zhongshuet al. Modeling air combat situation assessment based on combat area division[J]. Journal of Beijing University of Aeronautics and Astronautics, 2013, 39(10): 1309-1313. (in Chinese)

基于空域划分的超视距空战态势威胁评估

基金项目: 装备预先研究计划项目(51310010504)
详细信息
    作者简介:

    肖亮(1983-),男,辽宁沈阳人,博士生,vin.x@qq.com.

  • 中图分类号: V271.4

Modeling air combat situation assessment based on combat area division

  • 摘要: 编队超视距空战(BVR,Beyond Visual Range)已成为现代空战的主要模式.在空战优势区域与劣势区域判断的基础上对整个空域进行划分,并给出4种特定空域态势.从空中态势和编队作战能力两方面对空战态势进行分析.使用主成分分析法选取输入变量分析编队作战能力,降低评估过程中收集数据的复杂度.应用遗传神经网络对影响BVR各因素进行效能评估,将遗传算法(GA,Genetic Algorithms)与多层前馈(BP,Back Propagation)网络结合,利用GA的全局搜索优化BP网络的结构参数,有效克服BP算法的局部收敛等问题.结果表明:该模型能在综合分析空战各指标后给出红蓝双发的态势评估指标,该模型可有效减少评估中的人为因素,使评估结果更为客观可信.

     

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出版历程
  • 收稿日期:  2012-12-12
  • 网络出版日期:  2013-10-30

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