Analysis of GNSS and IMU Sensor Data Fusion Using the Unscented Kalman Filter Method on Medical Drones in Open Air

Abstract

Accurate and efficient position estimation is an important element in flight missions for autonomous aerial vehicles such as Unmanned Aerial Vehicles (UAVs). UAVs rely on sensors that provide information regarding position, speed, and orientation. GPS and IMU are sensors that can provide navigation information, where each sensor has characteristics that can complement each other’s shortcomings. Research related to the integration of GPS and IMU sensors for positioning with increasingly higher accuracy has been widely applied, one of which is the Kalman Filter method. Kalman filter not only performs digital signal filtering but is also capable of smoothing and predicting recursion to obtain the most accurate estimation results. Along with the development of the modification of the Kalman Filter, the Unscented Kalman Filter (UKF) method is a solution in overcoming nonlinear systems with estimation results approaching the actual value, so that the positioning accuracy is higher and convergent. This study intends to analyze the fusion performance of GPS and IMU sensor data using the UKF method on Medical Drones in an open space in order to obtain a position with high accuracy. UKF simulations on Medical Drones were carried out using a mathematical model on a 6 DOF (Degree of Freedom) UAV. The sensor data fusion performance was tested by comparing the results of the UKF method with the EKF on the Geopointer software. The results showed that the UKF simulation gave a positional accuracy of 0.022 m to the measurement data. The UKF simulation results in a better positioning accuracy when compared to EKF processing on the Geopointer software which only achieves a position accuracy of 8.467 m. The low accuracy of the EKF processing results is due to the mismatch of the IMU parameters obtained with the actual Medical Drone parameters.