红外成像导引头抗干扰性能评估方法研究
|
摘要
面对着日益复杂的战争环境,红外成像导引头的抗干扰性能需要不断提高。研究红外导弹武器抗干扰性能的评估方法,可为导弹武器系统研究提供技术支持,具有重要意义。本文提出了一种基于随机森林的抗干扰性能评估方法,通过机器学习的思路,计算导引头的综合抗干扰性能值,为红外成像导引头抗干扰性能评估提供了新的思路。实验结果表明,该算法结果可靠,精度高,能够有效的评估红外成像导引头的抗干扰性能。
Facing the increasingly complicated war environment,the anti-interference performance of the infrared seeker needs to be continually improved.To meet this challenge,the research on the anti-interference performance of infrared weapon can provide technical support of the weapon system,which makes the great construction.This paper presents a method to evaluate the anti-interference performance based on random forest method.Through the idea of machine learning,the comprehensive anti-interference performance value of the guidance system is calculated,which provides a new idea for the evaluation of anti-interference performance of infrared imaging seeker.The experimental results show that the algorithm is reliable and accurate,and it can effectively evaluate the anti-interference performance of infrared seeker.
Facing the increasingly complicated war environment,the anti-interference performance of the infrared seeker needs to be continually improved.To meet this challenge,the research on the anti-interference performance of infrared weapon can provide technical support of the weapon system,which makes the great construction.This paper presents a method to evaluate the anti-interference performance based on random forest method.Through the idea of machine learning,the comprehensive anti-interference performance value of the guidance system is calculated,which provides a new idea for the evaluation of anti-interference performance of infrared imaging seeker.The experimental results show that the algorithm is reliable and accurate,and it can effectively evaluate the anti-interference performance of infrared seeker.
引文
[1]李立坤.精确制导技术现状及发展方向[J].航空兵器,2004(1):1-4.
[2]刘永昌,吴鹏.未来光电精确寻的制导技术发展前景预测[J].现代防御技术,2003,31(6):46-51.
[3]高卫,黄惠明,李军.光电干扰效果评估方法[M].北京:国防工业出版社,2006:1-145.
[4]韩培骏.红外导引系统抗干扰性能评估准则与方法研究[D].南京航空航天大学,2012.
[5]贾秋锐,周立柱,孙媛媛.红外成像制导抗干扰分析[J].制导与引信,2010,31(1):1-3.
[6]刘明辉,杨峰,王磊,等.基于SVR的反舰导弹作战效能探索性评估方法[J].计算机仿真,2009,26(8):9-12.
[7]胡朝晖,闫杰.红外空空导弹抗干扰性能的综合评估方法研究[J].弹箭与制导学报,2009,29(1):61-64.
[8]韩本刚,董敏周,于云峰,等.用基于指数标度的层次分析法评估红外导弹导引头抗干扰性能[J].西北工业大学学报,2008,26(1):69-73.
[9]FEARN T.Classification and regression trees(CART)[J].Journal of Near Infrared Spectroscopy,2006,17(1):13.
[9]BREIMAN L.Classification and regression trees[M].London:Routledge,2017.
[10]周志华.机器学习[M].北京:清华大学出版社[M],2016.
[11]Breiman L.Random forest[J].Machine Learning,2001,45:5-32.
[2]刘永昌,吴鹏.未来光电精确寻的制导技术发展前景预测[J].现代防御技术,2003,31(6):46-51.
[3]高卫,黄惠明,李军.光电干扰效果评估方法[M].北京:国防工业出版社,2006:1-145.
[4]韩培骏.红外导引系统抗干扰性能评估准则与方法研究[D].南京航空航天大学,2012.
[5]贾秋锐,周立柱,孙媛媛.红外成像制导抗干扰分析[J].制导与引信,2010,31(1):1-3.
[6]刘明辉,杨峰,王磊,等.基于SVR的反舰导弹作战效能探索性评估方法[J].计算机仿真,2009,26(8):9-12.
[7]胡朝晖,闫杰.红外空空导弹抗干扰性能的综合评估方法研究[J].弹箭与制导学报,2009,29(1):61-64.
[8]韩本刚,董敏周,于云峰,等.用基于指数标度的层次分析法评估红外导弹导引头抗干扰性能[J].西北工业大学学报,2008,26(1):69-73.
[9]FEARN T.Classification and regression trees(CART)[J].Journal of Near Infrared Spectroscopy,2006,17(1):13.
[9]BREIMAN L.Classification and regression trees[M].London:Routledge,2017.
[10]周志华.机器学习[M].北京:清华大学出版社[M],2016.
[11]Breiman L.Random forest[J].Machine Learning,2001,45:5-32.