TY - GEN
T1 - Event-triggered Extended Kalman Filter for UAV Monitoring System
AU - Zang, Yunge
AU - Li, Yan
AU - Duan, Yuting
AU - Li, Xiangyu
AU - Chang, Xi
AU - Li, Zhen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - To facilitate ground station monitoring and command uploading, unmanned aerial vehicles (UAVs) need to frequently exchange individual state data between units. However, this results in a significant usage of communication bandwidth. To address this issue, on the basis of an event-triggered strategy, this paper proposes an Extended Kalman Filter (EKF). aimed at reducing the communication burden of UAVs while maintaining high accuracy. Specifically, a state measurement triggered by an event is selected for filtering only if it contains innovation, thereby reducing the amount of data that needs to be communicated. Since UAV systems are nonlinear, EKF is adopted to fully utilize the information obtained from event-triggered strategies, thereby enhancing the estimation performance. In this paper, a physical UAV was used to verify the proposed algorithm, and it proved to have robust dynamic performance and to effectively reduce the communication rate.
AB - To facilitate ground station monitoring and command uploading, unmanned aerial vehicles (UAVs) need to frequently exchange individual state data between units. However, this results in a significant usage of communication bandwidth. To address this issue, on the basis of an event-triggered strategy, this paper proposes an Extended Kalman Filter (EKF). aimed at reducing the communication burden of UAVs while maintaining high accuracy. Specifically, a state measurement triggered by an event is selected for filtering only if it contains innovation, thereby reducing the amount of data that needs to be communicated. Since UAV systems are nonlinear, EKF is adopted to fully utilize the information obtained from event-triggered strategies, thereby enhancing the estimation performance. In this paper, a physical UAV was used to verify the proposed algorithm, and it proved to have robust dynamic performance and to effectively reduce the communication rate.
KW - Event-trigger strategy
KW - communication rate
KW - extended Kalman filter (EKF)
KW - real-time state estimation
KW - unmanned aerial vehicles (UAVs)
UR - http://www.scopus.com/inward/record.url?scp=85165995450&partnerID=8YFLogxK
U2 - 10.1109/DDCLS58216.2023.10167412
DO - 10.1109/DDCLS58216.2023.10167412
M3 - Conference contribution
AN - SCOPUS:85165995450
T3 - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
SP - 2032
EP - 2036
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 -