Quadrotor UAV state estimation based on High-Degree Cubature Kalman filter

In this paper, on the basis of previous results solving INS/GNSS integration, Cubature Kalman Filter (CKF) and High Degree Cubature Kalman Filter with (GHKF) arc the references for the recent developed generalized Cubature rule based Kalman Filter (GCKF). High degree cubature rules arc the kernel of the new solution for more accurate estimation with less computational complexity compared with the Gauss-Hermite Quadrature (GHQ). In this paper, state estimation of a UAV between previous and novel approaches. Instead of use particle filter as the reference filter, it is maintained that CKF is the best reference for all Gaussian approximate filters after Gauss-Hermite Kalman Filter GHKF which is not selected in this work because of its limited real-time implementation in high-dimensional state-spaces. GNSS (GPS+GLONASS) measurements are assumed available to provide heading measurement by the use of kinematic model and observe attitude angles delivered by the IMU. Gaussian approximation filters: SPKF with Cubature Kalman Filter (CKF) are compared with new high order CKF based on Spherical-radial cubature rules developed at the fifth order. Estimation accuracy of the high degree CKF is observed and discussed for different initialization parameters. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.