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.

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