Abstract
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.
Highlights
In modern navigation and data fusion, the Kalman filter (KF) has become one of the most widely used estimation methods due to its advantages of being more simple and useful (Chen 2012; Han & Wang 2012; Feng et al 2013; Santos 2015)
An improved adaptive filter based on the Cubature Kalman filter (CKF) To solve the nonlinear problem and improve the stochastic model simultaneously, we here proposed a new improved adaptive filter by combining the CKF and the Variance-Covariance Components Estimation (VCE) adaptive method
Simulations and experiments In order to show the advantages of the improved CKF, this section presents the solution comparison between
Summary
In modern navigation and data fusion, the Kalman filter (KF) has become one of the most widely used estimation methods due to its advantages of being more simple and useful (Chen 2012; Han & Wang 2012; Feng et al 2013; Santos 2015). The KF method still has some limitations (Tang et al 2014; Wang et al 2010; Wang 2009). It has been developed on the base of linear systems while almost all of the systems are nonlinear . If the KF is applied in practice, the nonlinearity may lead to large errors or even to the filter divergence. The nonlinear filters have been a hot area of the state estimation. Two nonlinear methods named the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are most widely used (Gao et al 2014; Dini et al 2011; Vaccarella et al 2013; Masazade et al 2012).
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