Adaptive high-degree cubature Kalman filter in the presence of unknown measurement noise covariance matrix

Here, the authors address the state estimation problem of non-linear systems in the presence of unknown measurement noise (MN) covariance matrix. Recently, a high-degree cubature Kalman filter (HCKF) has been successfully used in the non-linear-state estimation problem with arbitrary degrees of accuracy in computing the spherical and radial integrals. However, the efficiency of the HCKF depends on a priori knowledge of the MN. To improve the performance of HCKF for non-linear systems with unknown MN covariance matrix, the authors proposed an adaptive HCKF, which combines the high-degree cubature rule with the variational Bayesian (VB) method to jointly estimate the system state and the unknown covariance matrix online. Experimental results demonstrate the effectiveness of the proposed filter.