Research on application of spatio-temporal Kalman filter in deformation analysis

doi:10.11947/j.AGCS.2022.20220292

Abstract

Abstract: Spatio-temporal Kalman filter can be used for spatio-temporal data denoising, interpolation and deformation prediction. In order to use the spatio-temporal Kalman filter model for spatio-temporal deformation analysis, the performance and applicability of three typical spatio-temporal Kalman filter models, namely Kriged Kalman filter (KKF), space time Kalman filter (STKF) and spatio-temporal mixed effects (STME), are compared and analyzed from the aspects of principles and experiments. The results show that: in theory, the three spatio-temporal Kalman filter models are based on the combination of spatial basis function and dynamic model to describe the spatio-temporal correlation. The main difference lies in the expression of spatial data, such as trend term, fine-scale variation, observation noise and spatial basis function. In terms of applicability, the KKF model is more suitable for the spatio-temporal deformation analysis of sparse stations, while the STKF model and STME model are more suitable for the spatio-temporal deformation analysis of massive stations. In terms of application effects of spatio-temporal deformation analysis, the three spatio-temporal Kalman filter models have high-precision effect in denoising, data interpolation and deformation prediction performance. The average improvement rate of denoising results compared with ordinary Kalman model is 21.1%, the average improvement rate of interpolation results compared with Hermite time interpolation results is 42.4%, the average improvement rate of its spatio-temporal prediction results relative to Kriging spatial interpolation results is 65.3%, the average improvement rate of its spatio-temporal prediction results for observation stations relative to the time prediction results of ordinary Kalman filter is 20.6%, and the average improvement rate of its spatio-temporal prediction results for non-observation stations relative to the prediction results of Kalman filter+Kriging model is 20.5%.

Key words: deformation analysis, filtering denoising, data interpolation, deformation prediction, spatio-temporal Kalman filter

CLC Number:

P227

SHI Qiang, DAI Wujiao, YAN Huineng, LIU Ning. Research on application of spatio-temporal Kalman filter in deformation analysis[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2125-2138.

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