摘要
针对现有空中目标识别方法敏捷性和可靠度不够高的问题,研究设计了一种深度学习模型MLSTM-FCN,结合了全卷积神经网络、循环神经网络和压缩与激励模块的优点。全卷积网络能够提取空战数据中的复杂局部特征,长短记忆神经网络可以捕捉空战意图数据的时序特征。通过消融实验和对比实验结果表明,MLSTM-FCN模型在意图识别准确率、反应速度和抗干扰能力方面明显优于现有的空中目标意图识别模型,取得了sota的结果,为指挥员在进行空中作战决策时提供更有效的依据。
This paper designs a deep learning model MLSTM-FCN in combination with the advantages of fully convoluted neural network, recurrent neural network and compression and excitation module aimed at the problems that the existing air target recognition methods are not high enough in agility and reliability. The complex local features can be extracted from the air combat data by the fully convoluted network, and the long and short memory neural network can capture the temporal features of air combat intention data. The results of ablation experiments and comparative experiments show that the MLSTM-FCN model is superior to the existing air target intention recognition model in terms of intention recognition accuracy, reaction speed and anti-interference ability, and the results of sota are obtained, providing a more effective basis for commanders in making air combat decisions.
作者
李乐民
宋亚飞
王鹏
王科
LI Lemin;SONG Yafei;WANG Peng;WANG Ke(Air Defense and Antimissile School,Air Force Engineering University,Xi’an 710051,China)
出处
《空军工程大学学报》
CSCD
北大核心
2024年第5期98-106,共9页
Journal of Air Force Engineering University
基金
国家自然科学基金(61806219,61703426,61876189)
陕西省自然科学基础研究计划(2021JM-226)
陕西省高校科协青年人才托举计划(20190108,20220106)
陕西省创新能力支撑计划(2020KJXX-065)。
关键词
意图识别
空中目标
深度学习
全卷积网络
长短记忆神经网络
压缩与激励模块
intent recognition
aerial targets
deep learning
fully convoluted network
long short-term memory
squeeze-and-excitation block
