CNN-based Real-time Prediction Method of Flight Trajectory of Unmanned Combat Aerial Vehicle

doi:10.3969/j.issn.1000-1093.2020.09.022

Abstract

Abstract: Trajectory prediction is part of air combat technology，and the predictors can select a more predictable maneuvering considerion of trajectory prediction results. A convolution neural network predicting method is proposed to obtain the position of unmanned combat aerial vehicle in a future time quickly and accurately. An improved model for limiting the angular velocity is presented since the original dynamic model can not correctly simulate the somersault maneuvering with roll angle deviation. The improved model is used for flight simulation under different conditions，and a large number of trajectory samples are obtained. The convolution neural networks with different layer number and convolution kernel number is trained and tested，and the network with the smallest prediction error is selected. Operational speed and error of the proposed method are compared with those of long short term memory neural network，recurrent neural network and fully connected neural network. The results show that the average prediction error of the proposed method is about 4.2 m on x axis，8.0 m on y axis and 19.5 m on z axis after 0.25 s without increasing operational time，and the errors are all smaller than those of the other methods.

Key words: unmamedcombataerialvehicle, flighttrajectoryprediction, convolutionalneuralnetwork, recurrentneuralnetwork, dynamicsmodel, flightsimulation

CLC Number:

E844

ZHANG Hongpeng， HUANG Changqiang， TANG Shangqin， XUAN Yongbo. CNN-based Real-time Prediction Method of Flight Trajectory of Unmanned Combat Aerial Vehicle[J]. Acta Armamentarii, 2020, 41(9): 1894-1903.

References

[1] HUANG C Q，DONG K S，HUANG H Q，et al.Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization[J].Journal of Systems Engineering and Electronics，2018，29(1):86-97.
[2] 孙楚，赵辉，王渊，等.基于强化学习的无人机自主机动决策方法[J].火力与指挥控制，2019，44(4):142-149.
SUN C，ZHAO H，WANG Y，et al. UCAV autonomic maneuver decision-making method based on reinforcement learning[J].Fire Control and Command Control，2019，44(4):142-149.(in Chinese)
[3] 杜海文，崔明朗，韩统，等.基于多目标优化与强化学习的空战机动决策[J].北京航空航天大学学报，2018，44(11):2247-2256.
DU H W，CUI M L，HAN T，et al.Maneuvering decision in air combat based on multi-objective optimization and reinforcement learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2018，44(11):2247-2256.(in Chinese)
[4] 李世豪，丁勇，高振龙.基于直觉模糊博弈的无人机空战机动决策[J].系统工程与电子技术，2019，41(5):1063-1070.
LI S H，DING Y，GAO Z L.UAV air combat maneuvering decision based on intuitionistic fuzzy game theory[J]. Systems Engineering and Electronics，2019，41(5):1063-1070.(in Chinese)
[5] 张涛，郭基联，徐西蒙，等.基于Elman神经网络的战斗机空战轨迹预测[J].飞行力学，2018，36(5):86-91.
ZHANG T，GUO J L，XU X M，et al. Air combat trajectory prediction of a fighter based on Elman neural network[J].Flight Dynamics， 2018，36(5):86-91.(in Chinese)
［6］王新，杨任农，左家亮，等.基于HPSO-TPFENN的目标机轨迹预测[J].西北工业大学学报，2019，37(3):613-620.
WANG X，YANG R N，ZUO J L，et al. Trajectory prediction of target aircraft based on HPSO-TPFENN neural network[J].Journal of Northwestern Polytechnical University，2019，37(3):613-620.(in Chinese)
［7］钱夔，周颖，杨柳静，等.基于BP神经网络的空中目标航迹预测模型[J].指挥信息系统与技术，2017，8(3):54-58.
QIAN K，ZHOU Y，YANG L J，et al. Aircraft target track prediction model based on BP neural network[J].Command Information System and Technology，2017，8(3):54-58.(in Chinese)
[8] LYMPEROPOULOS I，LYGEROS J.Adaptive aircraft trajectory prediction using particle filters[C]∥Proceedings of Guidance，Navigation and Control Conference and Exhibit. Honolulu, HI，US:AIAA，2008:1-16.
[9] LEFFERTS E J，MARKLEY F L，MALCOLM D S. Kalman filtering for spacecraft attitude estimation[J].Journal of Guidance Control and Dynamics，1982，5(5):417-429.
[10] QIAO S J，SHEN D Y，WANG X T，et al.A self-adaptive parameter selection trajectory prediction approach via hidden Markov models[J].IEEE Transactions on Intelligent Transportation Systems，2015，16(1):284-296.
[11] AKCAL M U，URE N K，YANG C G，et al. Predictive missile guidance with online trajectory learning[J]. Defence Science Journal，2017，67(3):332-338.
［12］ WANG Q Y，ZHANG Z L，WANG Z Y，et al. The trajectory prediction of spacecraft by grey method[J].Measurement Science and Technology，2016，27(8):085011.
［13］ ALAHI A，GOEL K，RAMANATHAN V，et al. Social LSTM: human trajectory prediction in crowded spaces[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway，NJ, US:IEEE，2016:961-971.
[14] NIKHIL N，MORRIS B T.Convolutional neural network for trajectory prediction[C]∥Proceedings of ECCV 2018 Workshops. Switzerland: Springer，2019:186-196.
[15] TIAN X，JEFFREY C.Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties[J].Physical Review Letters，2018，120(14):1-6.
[16] WILLIAMS P. Three-dimensional aircraft terrain-following via real-time optimal control[J]. Journal of Guidance，Control, and Dynamics， 2007,30 (4) : 1201-1205.
[17] 古德费洛.深度学习[M].北京：人民邮电出版社，2017:201-226.
GOODFELLOW. Deep learning[M].Beijing: People's Post and Telecommunications Press，2017:201-226.(in Chinese)
[18] HUBEL D H，WIESEL T N.Recepective fields，binocular interaction，and functional architecture in the cat's visual cortex[J]. Journal of Physiology，1962，160: 106-154.
[19] 毛景慧.基于LSTM深度神经网络的股市时间序列预测精度的影响因素研究[D].广州:暨南大学，2017:25-26.
MAO J H.Research on influencing factors of stock market time series prediction accuracy based on LSTM deep neural network[D]. Guangzhou:Jinan University，2017:25-26.(in Chinese)
[20] HORNIK K，STINCHCOMBE M，WHITE H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks[J].Neural Networks，1990，3:551-560.
[21] DUCHI J, HAZAN E, SINGER Y. Adaptive subgradient methods for online learning and stochastic optimization[J].Journal of Machine Learning Research，2011，12(7):2121-2159.
[22] 国海峰，侯满义，张庆杰，等.基于统计学原理的无人作战飞机鲁棒机动决策[J].兵工学报，2017，38(1):160-167.
GUO H F，HOU M Y，ZHANG Q J，et al. UCAV robust maneuver decision based on statistics principle[J]. Acta Armamentarii，2017，38(1):160-167.(in Chinese)

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