TY - GEN
T1 - Hierarchical Extended Kalman Filter Cooperative Positioning Algorithm for UAV Swarm
AU - Luo, Qinghua
AU - Li, Shenghui
AU - Yan, Xiaozhen
AU - Zhou, Xinyue
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To enhance the operational range of the unmanned aerial vehicle (UAV) swarm and optimize the positioning accuracy and stability of the UAV positioning system, this study introduces a Hierarchical Extended Kalman Filter (HEKF) algorithm. Collaborative positioning is achieved through the utilization of high-precision guidance layer information in conjunction with low-precision information from the following layer. To enhance positioning accuracy and convergence speed, the integration of the consistency principle with HEKF is employed to promote state consistency among individual nodes within the UAV system. Considering that the excessive number of leaders in the guidance layer leads to information redundancy, an optimization strategy is used to optimize the leaders by using the average distance measurement and its standard deviation, and the measurement information is weighted before collaborative positioning. The experimental findings demonstrate a notable enhancement in both the accuracy and stability of the algorithm's positioning capability.
AB - To enhance the operational range of the unmanned aerial vehicle (UAV) swarm and optimize the positioning accuracy and stability of the UAV positioning system, this study introduces a Hierarchical Extended Kalman Filter (HEKF) algorithm. Collaborative positioning is achieved through the utilization of high-precision guidance layer information in conjunction with low-precision information from the following layer. To enhance positioning accuracy and convergence speed, the integration of the consistency principle with HEKF is employed to promote state consistency among individual nodes within the UAV system. Considering that the excessive number of leaders in the guidance layer leads to information redundancy, an optimization strategy is used to optimize the leaders by using the average distance measurement and its standard deviation, and the measurement information is weighted before collaborative positioning. The experimental findings demonstrate a notable enhancement in both the accuracy and stability of the algorithm's positioning capability.
KW - Consistency principle
KW - Hierarchical extended Kalman filter cooperative localization algorithm
KW - Optimization strategy
UR - https://www.scopus.com/pages/publications/85190279987
U2 - 10.1109/GCWkshps58843.2023.10464497
DO - 10.1109/GCWkshps58843.2023.10464497
M3 - 会议稿件
AN - SCOPUS:85190279987
T3 - 2023 IEEE Globecom Workshops, GC Wkshps 2023
SP - 1801
EP - 1806
BT - 2023 IEEE Globecom Workshops, GC Wkshps 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Globecom Workshops, GLOBECOM Workshop 2023
Y2 - 4 December 2023 through 8 December 2023
ER -