TY - GEN
T1 - Tactical Intention Recognition Method of Air Combat Target Based on BiLSTM network
AU - Wang, Xingyu
AU - Yang, Zhen
AU - Zhan, Guang
AU - Huang, Jichuan
AU - Chai, Shiyuan
AU - Zhou, Deyun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As an important support for modern air combat intelligent auxiliary decision-making, real-time and high-precision target intent recognition addresses the foundation for realizing deep situational awareness and creating tactical opportunities. Aiming at the limitation of the existing algorithms such as dependence on empirical knowledge, difficulty in extracting the full temporal characteristics, and inability to meet the requirements of actual air combat, this paper proposes a target tactical intention recognition algorithm based on bi-directional Long Short-Term Memory (BiLSTM). Firstly, we analyze the air combat mechanism to construct the target tactical intention space based on the tactical layer. Specifically, suitable characteristics are selected to describe the intention space. We then design a recognition method considering the characteristic of the tactical intention space. Finally, compared with other algorithms, the simulation results show the effectiveness of the proposed method, which outperforms other methods in terms of accuracy at 92%. and the results are more practical.
AB - As an important support for modern air combat intelligent auxiliary decision-making, real-time and high-precision target intent recognition addresses the foundation for realizing deep situational awareness and creating tactical opportunities. Aiming at the limitation of the existing algorithms such as dependence on empirical knowledge, difficulty in extracting the full temporal characteristics, and inability to meet the requirements of actual air combat, this paper proposes a target tactical intention recognition algorithm based on bi-directional Long Short-Term Memory (BiLSTM). Firstly, we analyze the air combat mechanism to construct the target tactical intention space based on the tactical layer. Specifically, suitable characteristics are selected to describe the intention space. We then design a recognition method considering the characteristic of the tactical intention space. Finally, compared with other algorithms, the simulation results show the effectiveness of the proposed method, which outperforms other methods in terms of accuracy at 92%. and the results are more practical.
KW - Bi-directional Long Short-Term Memory(Bilstm)
KW - Time series data
KW - aerial target
KW - tactical intention
UR - http://www.scopus.com/inward/record.url?scp=85146487330&partnerID=8YFLogxK
U2 - 10.1109/ICUS55513.2022.9986667
DO - 10.1109/ICUS55513.2022.9986667
M3 - 会议稿件
AN - SCOPUS:85146487330
T3 - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
SP - 63
EP - 67
BT - Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
A2 - Song, Rong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Unmanned Systems, ICUS 2022
Y2 - 28 October 2022 through 30 October 2022
ER -