Adaptive cubature Kalman filter based on the variance-covariance components estimation

Although the Kalman filter (KF) is widely used in practice, its estimated results are optimal only when the system model is linear and the noise characteristics of the system are already exactly known. However, it is extremely difficult to satisfy such requirement since the uncertainty caused by the inertial instrument and the external environment, for instance, in the aided inertial navigation. In practice almost all of the systems are nonlinear. So the nonlinear filter and the adaptive filter should be considered together. To improve the filter accuracy, a novel adaptive filter based on the nonlinear Cubature Kalman filter (CKF) and the Variance-Covariance Components Estimation (VCE) was proposed in this paper. Here, the CKF was used to solve the nonlinear issue while the VCE method was used for the noise covariance matrix of the nonlinear system real-time estimation. The simulation and experiment results showed that better estimated states can be obtained with this proposed adaptive filter based on the CKF.