Features of Invariant Extended Kalman Filter Applied to Unmanned Aerial Vehicle Navigation

Abstract

This research used an invariant extended Kalman filter (IEKF) for the navigation of an unmanned aerial vehicle (UAV), and compared the properties and performance of this IEKF with those of an open-source navigation method based on an extended Kalman filter (EKF). The IEKF is a fairly new variant of the EKF, and its properties have been verified theoretically and through simulations and experiments. This study investigated its performance using a practical implementation and examined its distinctive features compared to the previous EKF-based approach. The test used two different types of UAVs: rotary wing and fixed wing. The method uses sensor measurements of the location and velocity from a GPS receiver; the acceleration, angular rate, and magnetic field from a microelectromechanical system-attitude heading reference system (MEMS-AHRS); and the altitude from a barometric sensor. Through flight tests, the estimated state variables and internal parameters such as the Kalman gain, state error covariance, and measurement innovation for the IEKF method and EKF-based method were compared. The estimated states and internal parameters showed that the IEKF method was more stable and convergent than the EKF-based method, although the estimated locations, velocities, and altitudes of the two methods were comparable.

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

“…with F χ e (χ χ χ e , U) = ∂ F (χ χ χ e , U) ∂ χ χ χ e χ χ χ e ,U ∈ IR 18×18 (33) computed by the same procedure used to define Z. Notice that χ χ χ e =χ χ χ e (t) and U = U (t).…”

“…Wind effects are estimated by observers, stochastic or deterministic as ( [24], while [25] presents an estimation strategy to compensate the external wrench exerted on the aerial multi-link robot while estimating external forces. Different state-estimation techniques as sliding-mode based observers [26] are also used to improve the performance of the system, however, due to its performance the Kalman Filter is widely used [27], [28], [29], [30], [31], [32], [33], [34], [35]. In this paper, we consider a multi-link aerial system that is intended to track a time-based trajectory while rejecting parametric and external disturbances during a multiple-load transportation task.…”

Design and Development of Portable LoRa-based Teleoperation Controller

“…Generally, data processing with appropriate filters can stabilize the measurement signals from an IMU. Kalman (15)(16)(17)(18) and complementary filters (19)(20)(21)(22)(23) are two widely used filters. In a previous work, (24) an experimental comparison of both filters found that they can obtain smooth and accurate signals regardless of dynamic or static experiments.…”

Application of Updated Sage–Husa Adaptive Kalman Filter in the Navigation of a Translational Sprinkler Irrigation Machine

“…It was indicated that adaptive Kalman filtering can improve the accuracy of vehicle LDV. Ko et al [8] used an invariant extended Kalman filter for the navigation of an unmanned aerial vehicle. Tradacete et al [9] presented a global positioning system for an autonomous electric vehicle.…”