Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 58-61, 88.

• Intelligent Computing • Previous Articles     Next Articles

Research on Track Fitting Model Under Two-way RNN

ZHANG Jie1, WANG Gang2, YAO Xiao-qiang2, SONG Ya-fei2, ZHENG Kang-bo1   

  1. (Graduate School,Air Force Engineering University,Xi’an 710054,China)1;
    (Air Defense and Anti Missile Academy,Air Force Engineering University,Xi’an 710054,China)2
  • Online:2019-11-10 Published:2019-11-20

Abstract: The modeling of flight path fitting is always one of the key problems in the research of combat agent trai-ning.Aiming at the low precision of track fitting in current combat multi-agent simulation training,a training strategy based on improved enhanced cyclic neural network and cubic spline interpolation was proposed.Taking the pitch angle,rolling angle and yaw angle of the aircraft as the reference objects,the track in the training process is fitted based on cubic spline interpolation algorithm,the error is reduced by cyclic neural network training,and the track is fitted.A large number of simulation experiments and the final engineering practice show that the method has higher accuracy and rationality than the existing track simulation algorithms.Under the same background,the track length decreases by nearly 10 percentage points,and the accuracy is more than 5 percentage points higher than the algorithm in the same field.The proposed algorithm can effectively solve the problem of combat agent in the same background.In simulation training,the track and actual operational error are reduced.

Key words: Track fitting, Improved loop network, Cubic spline interpolation, Combat agent

CLC Number: 

  • TP391
[1]黄卫芳,王伟,刘鸿飞.雷达飞行航迹拟合分析系统初步研究与实现[J].空中交通管理,2008(5):10-12.
[2]汤继强.某机场终端区进近程序优化设计研究[D].成都:电子科技大学,2011.
[3]连德忠,吴文城,游德有,等.三次样条插值的新算法[J].龙岩学院学报,2017,35(5):4-7.
[4]邢丽.一维插值算法在实际问题中的应用和比较[J].上海第二工业大学学报,2013,30(4):311-314.
[5]邓兴升,汤仲安.移动格林基函数样条二维插值算法研究[J].大地测量与地球动力学,2011,31(6):69-72.
[6]王娜,王健,曹智明,等.加权平均预测的一阶多智能体一致性的采样控制[J].空军工程大学学报(自然科学版),2017,18(1):105-110.
[7]ZIMA K.Fuzzy case based reasoning in sports facilities unit cost estimating∥International Conference of Numerical Analysis & Applied Mathematics.2016.
[8]刘喜春,王超,王文广,等.基于多Agent强化学习的战时备件供应保障动态协调机制[J].空军工程大学学报(自然科学版),2009(3):59-63.
[9]GANJEHKAVIRI A,MOHD JAAFAR M N,HOSSEINI S E,et al.Genetic algorithm for optimization of energy systems:Solution uniqueness,accuracy,Pareto convergence and dimension reduction[J].Energy,2017,119:167-177.
[10]孙京诰,杨嘉雄,王硕,等.基于Actor-Critic和神经网络的闭环脑机接口控制器设计[J/OL].https://doi.org/10.13195/j.kzyjc.2017.0791.
[11]翟建伟.基于深度Q网络算法与模型的研究[D].苏州:苏州大学,2017.
[12]周海平.陆态网络基准网数据处理策略分析[J].地理空间信息,2018,16(9):120-122,12.
[13]SHI Y B,SI L,FENG G B,et al.Numerical and experimental study on liquid crystal optical phased array beam steering combined with stochastic parallel gradient descentalgorithm[J].Optik- International Journal for Light and Electron Optics,2016,127(3):1450-1454.
[14]MOKHTARI A,RIBEIRO A.Regularized stochastic BFGS algorithm[P].2013.
[15]郝志峰,黄浩,蔡瑞初,等.基于多特征融合与双向RNN的细粒度意见分析[J].计算机工程,2018,44(7):199-204,211.
[16]KUMAR R,BASKAR S.B-spline quasi-interpolation based numerical methods for some Sobolev type equations[J].Journal of Computational and Applied Mathematics,2016,292(C):41-66.
[17]张振兴,杨任农,张彬超,等.空战飞行对敌目标逼近航迹预测仿真[J].空军工程大学学报(自然科学版),2018,19(2):33-37.
[18]刘进忙,冯有前,张晓刚.基于最小二乘法Lagrange插值基函数的拟合推广[J].空军工程大学学报(自然科学版),2002,(4):84-87.
[19]王莉莉,彭勃.航迹点特征的时间窗分割算法的航迹聚类[J].空军工程大学学报(自然科学版),2018,19(3):19-23.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] DU Wei, DING Shi-fei. Overview on Multi-agent Reinforcement Learning[J]. Computer Science, 2019, 46(8): 1 -8 .
[2] GAO Li-zheng, ZHOU Gang, LUO Jun-yong, LAN Ming-jing. Survey on Meta-event Extraction[J]. Computer Science, 2019, 46(8): 9 -15 .
[3] CAI Li, LI Ying-zi, JIANG Fang, LIANG Yu. Study on Clustering Mining of Imbalanced Data Fusion Towards Urban Hotspots[J]. Computer Science, 2019, 46(8): 16 -22 .
[4] YANG Zhen, WANG Hong-jun. Important Location Identification of Mobile Users Based on Trajectory Division and Density Clustering Method[J]. Computer Science, 2019, 46(8): 23 -27 .
[5] DENG Cun-bin, YU Hui-qun, FAN Gui-sheng. Integrating Dynamic Collaborative Filtering and Deep Learning for Recommendation[J]. Computer Science, 2019, 46(8): 28 -34 .
[6] ZHONG Feng-yan, WANG Yan, LI Nian-shuang. Node Selection Scheme for Data Repair in Heterogeneous Distributed Storage Systems[J]. Computer Science, 2019, 46(8): 35 -41 .
[7] SUN Guo-dao, ZHOU Zhi-xiu, LI Si, LIU Yi-peng, LIANG Rong-hua. Spatio-Temporal Evolution of Geographical Topics[J]. Computer Science, 2019, 46(8): 42 -49 .
[8] ZHANG Hui-bing, ZHONG Hao, HU Xiao-li. User Reviews Clustering Method Based on Topic Analysis[J]. Computer Science, 2019, 46(8): 50 -55 .
[9] LI Bo-jia, ZHANG Yang-sen, CHEN Ruo-yu. Method for Generating Massive Data with Assignable Distribution[J]. Computer Science, 2019, 46(8): 56 -63 .
[10] LU Xian-guang, DU Xue-hui, WANG Wen-juan. Alert Correlation Algorithm Based on Improved FP Growth[J]. Computer Science, 2019, 46(8): 64 -70 .