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Unscented kalman filter application for state estimation of a qball-X4 quadrotor
Author(s):
1. Le Ngoc Giang: Department of Metrology, Faculty of Fundamental Technology, AD-AF Academy of Vietnam,,Vietnam
Abstract:
In the process of controlling the quadrotor, accurate state estimation plays a crucial role. For positioning purposes, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are employed to determine the position of moving subjects. The Qball-X4 quadrotor is a highly nonlinear object, and when combined with Gauss interference, it can compromise the accuracy of the EKF. To address these challenges, this study focuses on assessing the suitability of the UKF nonlinear filtering method for estimating the state of the Qball-X4 quadrotor. This estimation is based on measurements from the gyroscope and Global Positioning System (GPS). To simulate real-life conditions, measurement noise has been deliberately introduced into the sensors. Rigorous testing under various conditions has emphasized the superior performance of the UKF filter in estimating the state of the quadrotor. This paper presents a valuable method to enhance the accuracy and reliability of the navigation system for the Qball-X4 quadrotor. Figure-1. Configuration of the Qball-X4 type quadrotor.
Page(s): 612-618
DOI: DOI not available
Published: Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 10, Year: 2024
Keywords:
Kalman Filter , position estimation , QballX4 quadrotor , measurement noise
References:
[1] Inc Quanser .2010 .Qball-X4 user manual. Canada., : .
[2] Wan E. A. and van der Merwe R. .2000 .The unscented Kalman filter for nonlinear estimation. In Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, : 153-158.
[3] Julier S. J.,Uhlmann J. K. .2004 .Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3) : 401-422.
[4] Wan E. A.,Nelson A. T. .2001 .Dual estimation and the unscented transformation. In Advances in neural information processing systems, : 666-672.
[5] Li Y.,Wang J. .2011 .Unscented Kalman filter for power system dynamic state estimation. IET Generation, Transmission & Distribution. 5, (2) : 216-225.
[6] Wan E. A.,Merwe R. V. D. .2001 .The square-root unscented Kalman filter for state and parameterestimation. In Proceedings of the 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, 01(6) : 3461-3464.
[7] Julier S. J.,Uhlmann J. K.,Durrant-Whyte H. F. .2000 .A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on automatic control, 45(3) : 477-482.
[8] Merwe R. V. D.,Wan E. A. R. V. D. .2003 .Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. In Proceedings of the Workshop on Advances in Machine Learning, : 173-182.
[9] Merwe R. V. D.,Wan E. A.,Julier S. J. .2000 .Sigma-point Kalman filters for nonlinear estimation and sensor-fusion: Applications to integrated navigation. In Proceedings of the AIAA Guidance, Navigation, and Control Conference, 4 : 1940-1957.
[10] Lee J.,Park S. .2012 .Unscented Kalman filterbased simultaneous localization and mapping for a quadrotor unmanned aerial vehicle. International Journal of Control, Automation and Systems, 10(4) : 716-726.
[11] Kim J.,Sukkarieh S. .2004 .Airborne simultaneous localisation and map building. In Proceedings of the 2004 IEEE International Conference on Robotics and Automation (ICRA'04), 1 : 406-411.
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