基于自注意力机制增强的CNN-LSTM的榴弹轨迹多步超前预测

doi:10.12382/bgxb.2024.0659

摘要/Abstract

摘要：

由于榴弹飞行轨迹呈现复杂性、时变性和突变性等特点,给近程防空拦截系统带来了极大的挑战。针对目前轨迹数据时空特征捕捉困难且只能进行较少步数预测的问题,提出一种引入自注意力机制的基于卷积神经网络和长短期记忆神经网络(1dimension Convolutional neural network-Long short-term memory-Attention, 1D CNN-LSTM-ATT)的一维轨迹多步超前预测模型。将所提模型与CNN-LSTM、LSTM模型分别进行单步和多步预测对比分析;实现对于目标轨迹的从T时刻到未来任意T+K时刻的高精度实时多步超前预测。实验结果表明:无论是单步还是多步预测,1D CNN-LSTM-ATT模型预测的评价指标明显优于其他2个模型;1D CNN-LSTM-ATT模型预测500步(即10s)的累计预测误差在射程方向为82.83m,高度方向为11.68m,横偏方向为0.07m,为实施弹体拦截及时响应提供了重要保障。

关键词: 轨迹多步超前预测, 深度学习, 自注意力机制, CNN-LSTM模型

Abstract:

Since the grenade flight trajectory presents the characteristics of complexity, temporal variability and sudden change, it brings great challenges to the close-range air-defense interception system. Aiming at the current problem that the spatio-temporal features of trajectory data are hardly captured and only a small number of steps can be predicted, a trajectory multi-step-ahead prediction model based on 1-dimension convolutional neural network-long short-term memory-attention (1D CNN-LSTM-ATT) with the introduction of self-attention mechanism is proposed. The proposed model is compared and analyzed with CNN-LSTM and LSTM models in single-step and multi-step predictions, respectively, and realizes a high-precision real-time multi-step-ahead prediction of target trajectory from T moment to any T+K future moments. The study shows that the evaluation indexes of the 1D CNN-LSTM-ATT model are significantly better than those of the other two models for both single-step and multi-step prediction; the cumulative prediction error of 500 steps (i.e., 10s) of the 1D CNN-LSTM-ATT model is 82.83m in the direction of range, 11.68m in the direction of altitude, and 0.07m in the direction of transverse deviation, which provides an important guarantee of timely response to the implementation of the projectile interception. This provides an important guarantee for the timely response to the implementation of projectile interception.

Key words: trajectory multi-step-ahead prediction, deep learning, self-attention mechanism, CNN-LSTM model

中图分类号:

TJ301

孙溪晨, 李伟兵, 黄昌伟, 付佳维, 冯君. 基于自注意力机制增强的CNN-LSTM的榴弹轨迹多步超前预测[J]. 兵工学报, 2024, 45(S1): 51-59.

SUN Xichen, LI Weibing, HUANG Changwei, FU Jiawei, FENG Jun. Multi-step-ahead Prediction of Grenade Trajectory Based on CNN-LSTM Enhanced by Deep Learning and Self-attention Mechanism[J]. Acta Armamentarii, 2024, 45(S1): 51-59.

图/表 15

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参数	采样值	采样间隔
发射角/(°)	[30,60]	2
速度/(m·s^-1)	[600,900]	10

参数	采样值	采样间隔
发射角/(°)	[30,60]	2
速度/(m·s^-1)	[600,900]	10

参数	采样值
迭代次数	50
学习率	0.0001
批处理量	256
优化器	Adam

参数	采样值
迭代次数	50
学习率	0.0001
批处理量	256
优化器	Adam

模型	参数	层信息
	LSTM层	神经元数量32
	Dropout	dropout率0.2
LSTM	LSTM层	神经元数量32
	Dropout	dropout率0.2
	全连接层	神经元数量3
	1D Conv	卷积核数量16,长度2
	1D Conv	卷积核数量64,长度2
	LSTM层	神经元数量32
CNN-LSTM	Dropout	dropout率0.2
	LSTM层	神经元数量32
	Dropout	dropout率0.2
	全连接层	神经元数量3
	1D Conv	卷积核数量16,长度2
	1D Conv	卷积核数量64,长度2
	LSTM层	神经元数量32
CNN-LSTM-ATT	Dropout	dropout率0.2
	LSTM层	神经元数量32
	Dropout	dropout率0.2
	Attention	-
	全连接层	神经元数量3