Deformation Detection in the GPS Real-Time Series by the Multiple Kalman Filters Model

Abstract

Global Positioning System (GPS) is widely used for monitoring some natural phenomena and man-made structures. The detection of the deformation epoch in real time is of great importance in these applications. This study is concerned with designing algorithms to detect the deformation epoch in order to improve the quality of GPS measurements for the real-time deformation applications. In this regard, the multiple Kalman filters model based on the idea of model selection is proposed to improve the reliability of the detection of the deformation epoch. For the model selection, the proposed model makes use of the statistical criterion comparison in each case instead of the hypothesis test. The model with the lower value of the statistical criterion is to be preferred. According to the statistical criterion, the optimal Kalman filter model can be selected to describe the time series and to identify the deformation epoch at each epoch. The simulated data and the GPS kinematic time series are used to verify the effectiveness of the multiple Kalman filters model.