TY - GEN
T1 - An Intention Recognition Method Based on Bayesian Network for UAV Formation in Confrontation
AU - Yang, Ruochu
AU - Gao, Huibin
AU - Wu, Xiande
AU - Feng, Yijun
N1 - Publisher Copyright:
© 2023, Beijing HIWING Sci. and Tech. Info Inst.
PY - 2023
Y1 - 2023
N2 - It’s important to predict the intention of the UAV formation for both side of aerial confrontation to gain operational advantages, for the combat of UAV formation confrontation is dynamic and changeable. Aiming at the problem of the intention recognition for the UAV formation in combat, this paper proposed a method based on dynamic Bayesian networks. First, the UAV formation description model is used to describe the speed, altitude and geometry configuration of the leader-follower UAV formation. Then, the effect of combat result was analyzed by configuration and state attributes of the UAV formation during aerial confrontation. Finally, the combat intentions were predicted based on dynamic Bayesian networks with formation flight data. The simulation results showed that the dynamic Bayesian-based approach could effectively identify the adversarial intention of UAV formation.
AB - It’s important to predict the intention of the UAV formation for both side of aerial confrontation to gain operational advantages, for the combat of UAV formation confrontation is dynamic and changeable. Aiming at the problem of the intention recognition for the UAV formation in combat, this paper proposed a method based on dynamic Bayesian networks. First, the UAV formation description model is used to describe the speed, altitude and geometry configuration of the leader-follower UAV formation. Then, the effect of combat result was analyzed by configuration and state attributes of the UAV formation during aerial confrontation. Finally, the combat intentions were predicted based on dynamic Bayesian networks with formation flight data. The simulation results showed that the dynamic Bayesian-based approach could effectively identify the adversarial intention of UAV formation.
KW - Dynamic Bayesian network
KW - Formation configuration
KW - Intention recognition
KW - Leader-follower strategy
KW - UAV Formation
UR - https://www.scopus.com/pages/publications/85151057970
U2 - 10.1007/978-981-99-0479-2_259
DO - 10.1007/978-981-99-0479-2_259
M3 - 会议稿件
AN - SCOPUS:85151057970
SN - 9789819904785
T3 - Lecture Notes in Electrical Engineering
SP - 2799
EP - 2809
BT - Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
A2 - Fu, Wenxing
A2 - Gu, Mancang
A2 - Niu, Yifeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2022
Y2 - 23 September 2022 through 25 September 2022
ER -