Unscented Kalman Filter Application for State Estimation of a Qball 2 Quadrotor

Unscented Kalman Filter Application for State Estimation of a Qball 2 Quadrotor

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© 2024 by IJETT Journal
Volume-72 Issue-5
Year of Publication : 2024
Author : Dang Tien Trung, Le Ngoc Giang
DOI : 10.14445/22315381/IJETT-V72I5P118

How to Cite?

Dang Tien Trung, Le Ngoc Giang, "Unscented Kalman Filter Application for State Estimation of a Qball 2 Quadrotor," International Journal of Engineering Trends and Technology, vol. 72, no. 5, pp. 178-184, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I5P118

Abstract
Accurate state estimation is pivotal in controlling quadrotors effectively. For positioning tasks, employing filtering techniques like the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) is common practice. However, the Qball 2 quadrotor poses a challenge due to its highly nonlinear nature, exacerbated by Gauss interference, which can degrade the EKF's accuracy. Consequently, this research centers on evaluating the applicability of the UKF nonlinear filtering method to estimate the Qball 2 quadrotor's state. Utilizing data from gyroscope and Global Positioning System (GPS) measurements, this estimation process incorporates deliberately introduced sensor noise to mimic real-world conditions. Thorough testing across diverse scenarios underscores the UKF filter's superior performance in state estimation for the quadrotor. This paper introduces a significant approach to bolstering the navigation system's precision and dependability for the Qball 2 quadrotor, offering insights into enhancing its overall performance.

Keywords
Kalman filter, UKF filter, Measurement noise, Position estimation, Qball 2 quadrotor.

References
[1] Quanser Qball-X4 User Manual, Quanser Innovate Educate, no. 888, pp. 1-55, 2010. [Online]. Available: https://nps.edu/documents/105873337/0/Qball-X4+User+Manual.pdf
[2] E.A. Wan, and R. Van Der Merwe, “The Unscented Kalman Filter for Nonlinear Estimation,” Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), Lake Louise, AB, Canada, pp. 153-158, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[3] S.J. Julier, and J.K. Uhlmann, “Unscented Filtering and Nonlinear Estimation,” Proceedings of the IEEE, vol. 92, no. 3, pp. 401-422, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Eric Wan, Rudolph Van Der Merwe, and Alex Tremain Nelson, “Dual Estimation and the Unscented Transformation,” Proceedings of the 12th International Conference on Neural Information Processing Systems, pp. 666-672, 1999.
[Google Scholar] [Publisher Link]
[5] G. Valverde, and V. Terzija, “Unscented Kalman Filter for Power System Dynamic State Estimation,” IET Generation, Transmission & Distribution, vol. 5, no. 1, pp. 29-37, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[6] R. Van der Merwe, and E.A. Wan, “The Square-Root Unscented Kalman Filter for State and Parameter-Estimation,” 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings (Cat.No.01CH37221), Salt Lake City, UT, USA, vol. 6, pp. 3461-3464, 2001.
[CrossRef] [Google Scholar] [Publisher Link]
[7] S. Julier, J. Uhlmann, and H.F. Durrant-Whyte, “A New Method for the Nonlinear Transformation of Means and Covariances in Filters and Estimators,” IEEE Transactions on Automatic Control, vol. 45, no. 3, pp. 477-482, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Rudolph Van der Merwe, Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models, OGI School of Science & Engineering at OHSU, pp. 1-754, 2004.
[Google Scholar] [Publisher Link]
[9] Rudolph van der Merwe, and Eric Wan and Simon Julier, “Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation,” Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, pp. 1-30, 2004.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Weide You et al., “Data Fusion of UWB and IMU Based on Unscented Kalman Filter For Indoor Localization Of Quadrotor Uav,” IEEE Access, vol. 8, pp. 64971-64981, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jong-Hyuk Kim, and S. Sukkarieh, “Airborne Simultaneous Localisation and Map Building,” 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), Taipei, Taiwan, vol. 1, pp. 406-411, 2003.
[CrossRef] [Google Scholar] [Publisher Link]