The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3/W10
07 Feb 2020
 | 07 Feb 2020


M. Q. Huang, L. Zhou, L. X. Qi, H. Y. Huang, M. Y. Tang, and Y. J. Shi

Keywords: deformation analysis, Kalman filter, GM(1,1), Grey relational analysis, hybrid model

Abstract. Normally, when using standard Kalman filter to analyze and predict the buildings deformation, the influence of a single factor is generally considered, or some factors are selected subjectively. In many cases, the objective influence of multiple effective factors on the model cannot be really considered, which adversely affects the accuracy of the model prediction and then affects the adaptability and prediction accuracy of the model. Aim at this problem, in this paper, we introduced grey relational analysis to determine the factor choice by calculating the grey relational grade of each impact factor. Then, we regarded the selected factors as state input vectors. Finally, we incorporated the state input vectors into model to establish grey relational Kalman filter model with considering multi-factors. In addition, we compared and analyzed the grey relational Kalman filter model with the stepwise regression model and BP neural network model that both can take into consideration the influence of multiple factors. The result of example analysis shows that the grey relational Kalman filter model can effectively select the factor which has great influence on deformation into the model as the state input vector during the modeling process, and the prediction accuracy of the recursive algorithm of standard Kalman filter is improved. Compared with the stepwise regression model and BP neural network model, the self-adaptability of the grey relational Kalman filter model is improved and the accuracy of the prediction results is also higher.