TY - GEN
T1 - Grouping of the UAV Swarm Based on Automatic Fuzzy Clustering
AU - Liu, Zhiheng
AU - Zhou, Rui
AU - Chen, Jinyong
AU - Zhang, Ning
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - The confrontation between UAV swarms will be an important combat style in the future, and a reasonable division of such a target air fleet is instructive to the deployment of our own resources. In this paper, we combine the genetic fuzzy clustering and the cluster validation to achieve autonomous grouping of the UAV swarm. Firstly, the flight state parameters such as position, speed, and yaw angle are used as characteristic components to measure the differences among UAVs. Then, the proposed approach establishes a mathematical model for grouping of the UAV swarm by fuzzy C-means algorithm. In addition, we compare five advanced clustering validity indexes and a clustering validation method based on graph theory, from which an evaluation criterion more applicable to grouping of the UAV swarm is selected. Finally, numerical simulations are performed to verify that the proposed approach can divide the air fleet autonomously and rationally in complex task scenarios.
AB - The confrontation between UAV swarms will be an important combat style in the future, and a reasonable division of such a target air fleet is instructive to the deployment of our own resources. In this paper, we combine the genetic fuzzy clustering and the cluster validation to achieve autonomous grouping of the UAV swarm. Firstly, the flight state parameters such as position, speed, and yaw angle are used as characteristic components to measure the differences among UAVs. Then, the proposed approach establishes a mathematical model for grouping of the UAV swarm by fuzzy C-means algorithm. In addition, we compare five advanced clustering validity indexes and a clustering validation method based on graph theory, from which an evaluation criterion more applicable to grouping of the UAV swarm is selected. Finally, numerical simulations are performed to verify that the proposed approach can divide the air fleet autonomously and rationally in complex task scenarios.
KW - Cluster validation
KW - Fuzzy custering
KW - Grouping of the UAV swarm
UR - https://www.scopus.com/pages/publications/85151124034
U2 - 10.1007/978-981-19-6613-2_546
DO - 10.1007/978-981-19-6613-2_546
M3 - 会议稿件
AN - SCOPUS:85151124034
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 5662
EP - 5673
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -