双向RNN下的航迹拟合模型研究

摘要/Abstract

摘要： 飞机航迹拟合的模型建立一直是作战智能体训练研究的关键问题之一。针对当前作战多智能体在仿真训练中的航迹拟合精确度过低的问题,提出了一种基于改进强化循环神经网络与三次样条插值的训练策略。以飞机的俯仰角、滚动角、偏航角为参考对象,基于三次样条插值算法,通过循环神经网络进行强化深度学习训练来降低误差,对航迹进行拟合。通过大量的仿真实验和最终工程实践的对比证明,该方法相比已有的航迹仿真算法具有更高的准确性与合理性。在相同背景下,其航迹长度下降近10个百分点,准确性也较同领域算法高出5％以上,能有效解决作战智能体在模拟训练中减小航迹与实际作战误差的问题。

关键词: 改进循环网络, 航迹拟合, 三次样条插值, 作战智能体

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

中图分类号:

TP391

张杰, 王刚, 姚小强, 宋亚飞, 郑康波. 双向RNN下的航迹拟合模型研究[J]. 计算机科学, 2019, 46(11A): 58-61. https://doi.org/

ZHANG Jie, WANG Gang, YAO Xiao-qiang, SONG Ya-fei, ZHENG Kang-bo. Research on Track Fitting Model Under Two-way RNN[J]. Computer Science, 2019, 46(11A): 58-61. https://doi.org/

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