TY - GEN
T1 - Hybrid Variable Structure DBN Mission Decision-Making Method for UAV Swarm
AU - Liu, Bowei
AU - Sun, Jingliang
AU - Long, Teng
AU - Liu, Dawei
AU - Cao, Yan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To cope with the dynamic mission decision-making issue in complex environments for UAV swarm, a hybrid variable structure-based dynamic Bayesian network (HVSDBN) inference decision-making method is proposed. Firstly, the UAV swarm mission decision-making model is established to assess the UAV swarm state and threat state accurately. To further improve the accuracy of decision-making, the threat assessment model and swarm state assessment model are built by using mixed continuous and discrete variables, respectively. Furthermore, a dynamic HVSDBN decision-making algorithm based on hybrid performance-capability parameters is proposed, which can adjust the structure of the decision model according to the priori information and observation data to improve the adaptability of the solution strategy. Simulation results demonstrate that, the HVSDBN method can im-prove the variance of decision results by 25.03% compared with traditional method, which effectively improves the accuracy of UAV swarm mission decision-making under complex dynamic environment.
AB - To cope with the dynamic mission decision-making issue in complex environments for UAV swarm, a hybrid variable structure-based dynamic Bayesian network (HVSDBN) inference decision-making method is proposed. Firstly, the UAV swarm mission decision-making model is established to assess the UAV swarm state and threat state accurately. To further improve the accuracy of decision-making, the threat assessment model and swarm state assessment model are built by using mixed continuous and discrete variables, respectively. Furthermore, a dynamic HVSDBN decision-making algorithm based on hybrid performance-capability parameters is proposed, which can adjust the structure of the decision model according to the priori information and observation data to improve the adaptability of the solution strategy. Simulation results demonstrate that, the HVSDBN method can im-prove the variance of decision results by 25.03% compared with traditional method, which effectively improves the accuracy of UAV swarm mission decision-making under complex dynamic environment.
KW - Dynamic Bayesian Network
KW - Mission Decision-Making
KW - UAV Swarm
KW - Variable Structure
UR - http://www.scopus.com/inward/record.url?scp=85166006519&partnerID=8YFLogxK
U2 - 10.1109/DDCLS58216.2023.10166899
DO - 10.1109/DDCLS58216.2023.10166899
M3 - Conference contribution
AN - SCOPUS:85166006519
T3 - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
SP - 943
EP - 948
BT - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Y2 - 12 May 2023 through 14 May 2023
ER -