Application of Sigma Point Kalman Filter in deformation monitoring

Abstract

The Extended Kalman Filter has been one of the most widely used methods for estimation of non-linear systems through the linearization of non-linear models. In recent several decades people have realized that there are a lot of constraints in application of the EKF for its hard implementation and intractability. In this paper a different estimation method is proposed, which takes advantage of the Sigma Point Transformation method thus approximating the true mean and variance more accurately. The new method can be applied to non-linear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what's more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of deformation monitoring. Numerical experiments show that the application of the Sigma Point Kalman Filter in deformation prediction is more effective than that of the EKF.