A Full Convoluted Neural Network-Based Tactical Intent Recognition Model for Airborne Targets
LI Lemin
SONG Yafei
WANG Peng
WANG Ke
Abstract：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 su-perior to the existing air target intention recognition model in terms of intention recognition accuracy,re-action speed and anti-interference ability,and the results of sota are obtained,providing a more effective basis for commanders in making air combat decisions.
Keywords：intent recognitionaerial targetsdeep learningfully convoluted networklong short-term memorysqueeze-and-excitation block
Publication Date：2024-10-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 98-106 )