TY - JOUR
T1 - Quick intention identification of an enemy aerial target through information classification processing
AU - Wang, Yinhan
AU - Wang, Jiang
AU - Fan, Shipeng
AU - Wang, Yuchen
N1 - Publisher Copyright:
© 2022 Elsevier Masson SAS
PY - 2023/1
Y1 - 2023/1
N2 - Rapidly and accurately identifying the tactical intention of an enemy aerial target is an important issue for combat decision making. To this end, a quick intention identification model based on hybrid neural network is established in this paper. With available feature sequential measurements of the enemy target as inputs of the model, possibilities of different intentions are calculated timely. To increase the training efficiency and accuracy of recognition, the measurement information is processed using different neural network. Maneuvering data with large variations over time are processed using gated recurrent unit (GRU), while other data are processed using back propagation (BP) neural network. Besides, the fitting cubic sample interpolation is adopted to deal with incomplete information. Monte Carlo simulations demonstrate the robustness and accuracy of the established model, and training comparison with conventional models shows that the proposed method has higher training efficiency and better identification performance.
AB - Rapidly and accurately identifying the tactical intention of an enemy aerial target is an important issue for combat decision making. To this end, a quick intention identification model based on hybrid neural network is established in this paper. With available feature sequential measurements of the enemy target as inputs of the model, possibilities of different intentions are calculated timely. To increase the training efficiency and accuracy of recognition, the measurement information is processed using different neural network. Maneuvering data with large variations over time are processed using gated recurrent unit (GRU), while other data are processed using back propagation (BP) neural network. Besides, the fitting cubic sample interpolation is adopted to deal with incomplete information. Monte Carlo simulations demonstrate the robustness and accuracy of the established model, and training comparison with conventional models shows that the proposed method has higher training efficiency and better identification performance.
KW - Artificial neural network
KW - Back propagation neural network
KW - Gated recurrent unit
KW - Information process
KW - Intention recognition
UR - http://www.scopus.com/inward/record.url?scp=85143309483&partnerID=8YFLogxK
U2 - 10.1016/j.ast.2022.108005
DO - 10.1016/j.ast.2022.108005
M3 - Article
AN - SCOPUS:85143309483
SN - 1270-9638
VL - 132
JO - Aerospace Science and Technology
JF - Aerospace Science and Technology
M1 - 108005
ER -