Efficient and accurate state estimation of maneuvering targets is a key step in radar surveillance systems. Especially in the unmanned aerial vehicles (UAVs) tracking scenes, the estimation of UAV’s position and velocity is not enough, and more details, such as heading angle and turning rate, are required in the estimated state space. In this paper, our effort is to establish a novel estimator that incorporates an enhanced extended Kalman filter (EKF) into the interactive multiple model (IMM) to improve the effectiveness of maneuvering UAV tracking. More specifically, a time-varying scaling process noise is introduced into the EKF to increase the filers’ convergence rate without losing its persistent estimation performance. And the nearly constant speed horizontal turn (NCSHT) model, as a special maneuvering model, is introduced to ensure the tracking performance during maneuver. Applied into the ground-to-air UAVs’ tracking experiment, the real-time results demonstrate the effectiveness of the proposed algorithm in convergence and accuracy.
The Tracking Estimation of Maneuvering UAVs Based on Enhanced Convergence IMM-EKF
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2022 ; Xi'an, China September 23, 2022 - September 25, 2022
Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022) ; Kapitel : 239 ; 2591-2600
2023-03-10
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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