Modern air combat is becoming more and more complex，and the traditional attack zones can only provide the limits of missile launch，which cannot meet the needs of modern air combat decision-making. For this reason，five types of attack zones, maximun attack zone, 50° attack zone, 90° attack zone, horizontal unescapable attack zone, and minimun attack zone, based on escape angles of enemy aircraft are proposed. The existing solving method of attack zone cannot be used to simultaneously solve the problem of multiple attack zones.A multi-function deep fitting network (MFDFN) is proposed to realize the simultaneous solution of multiple attack zones. Firstly，an improved advance-retreat method is designed，and the sample library of capture zone is obtained through ballistic simulation. According to the characteristics of multi-function fitting network，a training strategy，called “overall pre-training and local fine-tuning”，is presented，by which network is supervised trained.The simulated results show that the MFDFN using the “overall pre-training and local fine-tuning” training strategy reduces the computing time while greatly improving the computing accuracy. The average relative error is about 0.27%，and the average absolute error is about 58.81 meters，which proves that the model is reliable and practical.
YAN Mengda， YANG Rennong， ZUO Jialiang， HU Dongyuan， YUE Longfei， ZHAO Yu.
Real-time Computing of Air-to-air Missile Multiple Capture Zones Based on Deep Learning. Acta Armamentarii. 2020, 41(12): 2466-2477 https://doi.org/10.3969/j.issn.1000-1093.2020.12.012
基金
国家自然科学基金项目(61503409)
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参考文献
［1］吴文海，周思羽，高丽，等.基于导弹攻击区的超视距空战态势评估改进[J].系统工程与电子技术，2011，33(12): 2679-2685. WU W H，ZHOU S Y，GAO L，et al.Improvements of situation assessment for beyond-visual-range air combat based on missile launching envelope analysis[J].Systems Engineering and Electronics，2011，33(12):2679-2685.(in Chinese) [2］赵克新，黄长强，魏政磊，等.改进决策树的无人机空战态势估计[J].哈尔滨工业大学学报，2019，51(4): 66-73. ZHAO K X，HUANG C Q，WEI Z L，et al. Situation assessment for unmanned aerial vehicle air combat based on anti-reasoning rules decision tree[J].Journal of Harbin Institute of Technology，2019，51(4):66-73.(in Chinese) [3］KUNGC C，CHIANG F L.A study of missile maximum capture area and fighter minimum evasive range for negotiation team air combat[C]∥Proceedings of the 15th International Conference on Control，Automation and Systems. Edinburgh，UK: IEEE，2015:207-212. [4］左家亮，杨任农，张滢，等.基于启发式强化学习的空战机动智能决策[J].航空学报，2017，38(10):217-230. ZUO J L，YANG R N，ZHANG Y，et al.Intelligent decision-making in air combat maneuvering based on heuristic reinforcement learning[J].Acta Aeronautica et Astronautica Sinica，2017，38(10): 217-230.(in Chinese) [5］杨任农，张振兴，房育寰，等.深度置信网络在导弹攻击区分类中的应用[J].国防科技大学学报，2019，41(2): 98-106. YANG R N，ZHANG Z X，FANG Y H，et al.Application of deep belief network in classification of missile launch envelopes[J].Journal of National University of Defense Technology，2019，41(2): 98-106.(in Chinese) [6］张安柯，孔繁峨，贺建良.目标强机动对中远程空空导弹可攻击区的影响[J].弹箭与制导学报，2016，36(2): 21-25. ZHANG A K，KONG F E，HE J L.Effect of strong maneuver target on attacking area of long-range air-to-air missile[J].Journal of Projectiles，Rockets，Missiles and Guidance，2016，36(2):21-25.(in Chinese) [7］方学毅，刘俊贤，周德云.基于背景插值的空空导弹攻击区在线模拟方法[J].系统工程与电子技术，2019，41(6): 1286-1293. FANG X Y，LIU J X，ZHOU D Y.Background interpolation for on-line simulation of capture zone[J].Systems Engineering and Electronics，2019，41(6):1286-1293.(in Chinese) [8］惠耀洛，南英，陈哨东，等.空空导弹动态攻击区的高精度快速算法研究[J].弹道学报，2015，27(2):39-45. HUI Y L，NAN Y，CHEN S D，et al. Research on rapid and high-precision calculation of dynamic attack zone for air-to-air missile[J].Journal of Ballistics，2015，27(2): 39-45.(in Chinese) [9］WU H X，QIU K，HUANG W，et al.Approximating solution for weapons employment zone of mid-range air-to-air missile[C]∥Proceedings of the 7th International Conference on Intelligent Human-machine Systems and Cybernetics.Hangzhou，China：IEEE，2015. [10］PARKS S，KIM D W，HONG J H，et al.Computation algorithm for dynamic launch zone of air-to-air missiles[J].Journal of the Korean Society for Aeronautical & Space Sciences，2014，42(9):762-772. [11］HUI Y L，NAN Y，CHEN X D，et al.Dynamic attack zone of air-to-air missile after being launched in random wind field[J].Chinese Journal of Aeronautics，2015，28(5): 1519-1528. [12］史振庆，梁晓龙，张佳强，等.基于协同攻击区的航空集群最优空间构型研究[J].兵工学报，2019，40(4):788-798. SHI Z Q，LIANG X L，ZHANG J Q，et al. Study of optimal spatial configuration of aircraft swarm based on cooperative attack zone[J].Acta Armamentarii，2019，40(4):788-798.(in Chinese) [13］高晓光，李新宇，岳勐琪，等.基于深度学习的地空导弹发射区拟合算法研究[J/OL].航空学报，2019. (2019-05-31)[2019-11-06]. http:∥kns.cnki.net/kcms/detail/11.1929.V.20190531.1450.002.html. GAO X G，LI X Y，YUE M Q，et al.Fitting algorithm of ground-to-air missile launching area based on deep learning [J/OL].Acta Aeronautica et Astronautica Sinica，2019. (2019-05-31)[2019-11-06].(in Chinese) http:∥kns.cnki.net/kcms/detail/11.1929.V.20190531.1450.002.html.(in Chinese) [14］孟博.基于BP神经网络的空空导弹攻击大机动目标攻击区仿真研究[J].弹箭与制导学报，2017，37(4): 43-46，50. MENG B.Research on launch envelopes simulation of air-to-air missile attacking high maneuvering targets based on BP neural network[J].Journal of Projectiles，Rockets，Missiles and Guidance， 2017，37(4):43-46，50.(in Chinese) [15］张振兴，杨任农，房育寰，等.基于改进BP神经网络的导弹攻击区计算[J].飞行力学，2018，36(2): 48-52. ZHANG Z X，YANG R N，FANG Y H，et al.Calculation of missile launch envelopes based on dynamic improved structured BP neural network[J].Flight Dynamics，2018，36(2): 48-52.(in Chinese) [16］SCHARL J，MAVRIS D，BURDUN I.Use of flight simulation in early design-formulation and application of the virtual testing and evaluation methodology[C]∥Proceedings of 2000 World Aviation Conference.San Diego，CA，US: AIAA，2000:5590. [17］ALKAHER D，MOSHAIOV A.Dynamic-escape-zone to avoid energy- bleeding coasting missile[J].Journal of Guidance，Control，and Dynamics，2015，38(10) 1908-1921. [18］周旺旺，姚佩阳，张杰勇，等.基于深度神经网络的空中目标作战意图识别[J].航空学报，2018，39(11):322468-322476. ZHOU W W，YAO P Y，ZHANG J Y，et al.Combat intention recognitionfor aerial targets based on deep neural network[J].Acta Aeronautica et Astronautica Sinica， 2018，39(11):322468-322476.(in Chinese) [19］GLOROT X，BORDES A，BENGIO Y.Deep sparse rectifier neural networks[C]∥Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale，FL，US:Society for Artificial Intelligence and Statistics，2011: 315-323. [20］KLAMBAUER G，UNTERTHINER T，MAYR A，et al.Self-normalizing neural networks[C]∥Proceedings of the 31st Advances in Neural Information Processing Systems. Long Beach，CA，US: Neural Information Processing Systems，2017:971-980. [21］SERGEYI，CHRISTIAN S.Batch normalization: accelerating deep network training by reducing internal covariate shift[C]∥Proceedings of the 32nd International Conference on Machine Learning.New York，NY，US:International Machine Learning Society，2015：448-456. [22］HINTON G E，SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J].Science，2006，313(5786):504-507. [23］KINGMA D P，BA J.Adam:a method for stochastic optimization[EB/OL].San Diego，CA，US:ICLR，2015.(2017-01-30)[2019-11-06].https:∥arxiv.org/abs/1412.6980.
