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兵工学报 ›› 2023, Vol. 44 ›› Issue (2): 462-471.doi: 10.12382/bgxb.2021.0489

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基于增强上下文信息长短期记忆网络的弹道轨迹预测

任济寰, 吴祥*(), 薄煜明, 吴盘龙, 何山   

  1. 南京理工大学 自动化学院, 江苏 南京 210094
  • 收稿日期:2022-07-23 上线日期:2022-08-30
  • 通讯作者:
  • 基金资助:
    国家自然科学基金项目(62103192); 中国博士后科学基金项目(2021M691597); 中央高校基本科研业务费专项资金资助项目(30922010710)

Ballistic Trajectory Prediction Based on Context-enhanced Long Short-Term Memory Network

REN Jihuan, WU Xiang*(), BO Yuming, WU Panlong, HE Shan   

  1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • Received:2022-07-23 Online:2022-08-30

摘要:

根据己方观测数据进行弹道轨迹预测是现代陆军实施精准打击的重要一环。针对现有弹道轨迹预测方法存在精度不足且实时性不强的问题,提出一种新的增强上下文信息长短期记忆(CE-LSTM)网络轨迹预测模型,进行弹道轨迹的长期精准预测。在LSTM网络可逼近任意非线性函数且具备长期记忆能力的基础上,构建隐藏层输出混合单元提取短时上下文信息,进一步逼近弹体运动状态;通过建立不同条件下的弹道轨迹的数据集,训练得到具备最优超参数的CE-LSTM网络。实验结果表明,与弹道微分方程组的数值积分解法以及高斯混合模型相比,CE-LSTM网络在预测的精度上优于其他2种方法,预测速度提高了3~10倍,且具备较强的泛化能力。

关键词: 轨迹预测, 增强上下文信息, 长短期记忆网络, 弹道微分方程组, 高斯混合模型

Abstract:

Trajectory prediction based on observed data is critical to the modern army's precision strike capability. Yet, the existing trajectory prediction methods have suffer from low accuracy and poor real-time performance. Thus, this study proposes a new model called Context-enhanced Long Short-Term Memory(CE-LSTM) Network to make an accurate long-term prediction of the exterior trajectory of incoming projectiles. The proposed method inherits the LSTM network's advantage in that it can approximate any nonlinear function and it has a long-term memory. Furthermore, we create a mixture output unit of the hidden layer to extract short-term context information and approximate the motion states of the incoming projectiles more accurately. The CE-LSTM network is trained with a large-scale dataset consisting of exterior trajectories under different initial conditions to obtain the optimal hyper-parameters. The experimental results show that compared with the methods like external ballistic differential equations and the Gaussian mixture model, the CE-LSTM network performs significantly better in prediction accuracy, and its prediction speed increases by three to ten times. Moreover, the proposed method is highly generalizable.

Key words: trajectory prediction, context-enhanced, long short-term memory network, ballistic differential equations, gaussian mixture model

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