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.
Highlights
The invariant extended Kalman filter is a fairly recent development that improves the extendedKalman filter using the geometrically transformed state error and output error based on the Lie group theory [1]
A matrix Lie group formulation of the invariant extended Kalman filter (IEKF) was used for simultaneous localization and mapping (SLAM) and improved the consistency of the estimation compared to the extended Kalman filter (EKF)-based SLAM method [9]
The IEKF was used for helicopter unmanned aerial vehicle (UAV) navigation [19], and its estimation performance was found based on experimental results, focusing on the estimated data of the state variables, excluding internal parameters such as the Kalman gain, state error covariance, and measurement innovation
Summary
The invariant extended Kalman filter is a fairly recent development that improves the extended. The IEKF was used for helicopter unmanned aerial vehicle (UAV) navigation [19], and its estimation performance was found based on experimental results, focusing on the estimated data of the state variables, excluding internal parameters such as the Kalman gain, state error covariance, and measurement innovation. This study compares the internal parameters of the Kalman gain, state error covariance, and measurement innovation to explicitly show the features of the IEKF. In studies where experimental results were compared, they usually concentrated on the estimated results of state variables, excluding internal parameters such as the Kalman gain, state error covariance, and measurement innovation [19]. This section compares the estimation results for the state variables and internal parameters such as the Kalman gain, state error covariance, and measurement innovation.
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