摘要
空战对抗过程中的目标状态数据呈现时序性、多维性等特征,为进一步提升目标意图识别的准确率,提出了一种基于改进注意力机制的长短期记忆网络(LSTM)目标识别方法,将空战可能出现的目标意图识别当成一个多分类问题处理。该方法首先通过目标实时的状态数据,生成特征序列;接着采用注意力机制提升目标的特征学习能力,增强空战过程中的主要目标状态特征表示,得到具有权值分配的特征向量;最后利用LSTM网络对目标特征向量进行训练,通过softmax层实现目标意图的识别。仿真实验表明,该方法利用注意力机制有效增强目标的特征学习,进一步提升了LSTM网络的识别精度,具有一定的科学性和有效性。
The target state data in the process of air combat confrontation presents the characteristics of time sequence and multi-dimensionality.In order to further improve the accuracy of target intention recognition,an LSTM target recognition method based on improved attention mechanism is proposed,and the target intention recognition that may occur in air combat is treated as a multi-classification problem.Firstly,the feature sequence is generated by the real-time state data of the target.Then,the attention mechanism is used to improve the feature learning ability of the target,enhance the state feature representation of the main target in the air combat process,and obtain the feature vector with weight allocation.Finally,the LSTM network is used to train the target feature vector,and the target intention is recognized through the softmax layer.The simulation results show that the proposed method effectively enhances the feature learning to the target by using the attention mechanism,and further improves the recognition accuracy of the LSTM network,which is scientific and effective.
作者
李战武
李双庆
彭明毓
江涛
鞠明
孙爱民
LI Zhanwu;LI Shuangqing;PENG Mingyu;JIANG Tao;JU Ming;SUN Ai’min
出处
《电光与控制》
CSCD
北大核心
2023年第3期1-7,共7页
Electronics Optics & Control
关键词
空战
目标意图识别
注意力机制
LSTM网络
权值分配
air combat
target intention recognition
attention mechanism
LSTM network
weight allocation
