Nonlinear Extended Kalman Filter for Attitude Estimation of the Fixed-Wing UAV

Autor: Tang Xiaoqian, Zhao Feicheng, Tang Zhengbing, Wang Hongying
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Optics, Vol 2022 (2022)
Druh dokumentu: article
ISSN: 1687-9392
42490936
DOI: 10.1155/2022/7883851
Popis: Flying vehicle’s navigation, direction, and control in real-time results in the design of a strap-down inertial navigation system (INS). The strategy results in low accuracy, performance with correctness. Aiming at the attitude estimation problem, many data fusion or filtering methods had been applied, which fail in many cases, which attains the nonlinear measurement model, process dynamics, and high navigation range. The main problem in unmanned aerial vehicles (UAVs) and flying vehicles is the determination of attitude angles. A novel attitude estimation algorithm is proposed in this study for the unmanned aerial vehicle (UAV). This research article designs two filtering algorithms for fixed-wing UAVs which are nonlinear for the attitude estimation. The filters are based on Kalman filters. The unscented Kalman filter (UKF) and cubature Kalman filter (CKF) were designed with different parameterizations of attitude, i.e., Euler angle (EA) and INS/unit quaternion (UQ) simultaneously. These filters, EA-UKF and INS-CKF, use the nonlinear process and measurement model. The computational results show that among both filters, the CKF attains a high accuracy, robustness, and estimation for the attitude estimation of the fixed-wing UAV.
Databáze: Directory of Open Access Journals
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