系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (6): 1968-1976.doi: 10.12305/j.issn.1001-506X.2022.06.24
吉瑞萍1,2, 张程祎1,2, 梁彦1,2,*, 王跃东1,2
收稿日期:
2021-08-16
出版日期:
2022-05-30
发布日期:
2022-05-30
通讯作者:
梁彦
作者简介:
吉瑞萍(1991—), 女, 博士研究生, 主要研究方向为估计理论、机器学习|张程祎(1996—), 男, 硕士研究生, 主要研究方向为目标跟踪、机器学习|梁彦(1971—), 男, 教授, 博士, 主要研究方向为估计理论、信息融合、远程预警数据处理应用|王跃东(1995—), 男, 博士研究生, 主要研究方向为估计理论、强化学习
基金资助:
Ruiping JI1,2, Chengyi ZHANG1,2, Yan LIANG1,2,*, Yuedong WANG1,2
Received:
2021-08-16
Online:
2022-05-30
Published:
2022-05-30
Contact:
Yan LIANG
摘要:
弹道导弹主动段长周期轨迹预报能够为导弹防御系统提供早期预警信息。传统的轨迹预报方法大多集中在导弹的自由段与再入段, 通过解析法、数值积分法或函数逼近法推断未来时刻目标的状态。由于弹道导弹在主动段会受到多个未知作用力的影响, 其轨迹预报相比自由段与再入段更具挑战性。为此, 本文提出了一种基于长短时记忆(long short-term memeory, LSTM)网络的弹道导弹主动段轨迹预报方法。首先, 根据导弹主动段动力学模型与弹道参数典型取值生成用于网络训练的大规模轨迹样本; 其次, 设计了基于深度LSTM网络的弹道导弹主动段轨迹递归预报方法; 最后, 与基于数值积分法、多项式拟合及反向传播神经网络的轨迹预报方法的实验对比, 表明了所提方法在主动段轨迹预报上的优越性。
中图分类号:
吉瑞萍, 张程祎, 梁彦, 王跃东. 基于LSTM的弹道导弹主动段轨迹预报[J]. 系统工程与电子技术, 2022, 44(6): 1968-1976.
Ruiping JI, Chengyi ZHANG, Yan LIANG, Yuedong WANG. Trajectory prediction of boost-phase ballistic missile based on LSTM[J]. Systems Engineering and Electronics, 2022, 44(6): 1968-1976.
表5
不同网络参数下验证集预报性能分析"
变量名称 | 变量值 | |||||||
网络参数 | LSTM 1节点数 | 128 | 64 | 256 | 128 | 128 | 128 | 128 |
LSTM 2节点数 | 256 | 128 | 512 | 256 | 256 | 256 | 256 | |
LSTM 3节点数 | 256 | 128 | 512 | 256 | 256 | 256 | 256 | |
Maxout节点数 | 64 | 32 | 128 | 64 | 64 | 64 | 64 | |
小批量样本数 | 64 | 64 | 64 | 32 | 128 | 64 | 64 | |
训练迭代步数 | 50 000 | 50 000 | 50 000 | 50 000 | 50 000 | 25 000 | 100 000 | |
预报性能 | APRMSEx/m | 124.8 | 1 273.9 | 366.2 | 286.8 | 846.1 | 427.6 | 131 581.1 |
APRMSEy/m | 1 780.3 | 8 844.5 | 2 915.2 | 1 920.5 | 6 647.5 | 3 099.2 | 42 442.8 | |
APRMSEz/m | 846.8 | 6 599.5 | 1 348.2 | 3 691.6 | 3 302.2 | 1 535.8 | 8 494.9 | |
APRMSEvx/(m·s-1) | 4 | 29.1 | 12.4 | 12.1 | 19.1 | 24.3 | 56.8 | |
APRMSEvy/(m·s-1) | 24.5 | 126.8 | 96.3 | 57 | 81.2 | 70.1 | 471 | |
APRMSEvz/(m·s-1) | 16.2 | 90.7 | 41.1 | 29.5 | 47.1 | 59.6 | 207 | |
平均运行时间/s | 0.4 | 0.3 | 0.5 | 0.4 | 0.4 | 0.4 | 0.4 |
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