Deformation Prediction Based on MIMU Signal

Abstract

In order to provide for the nature of the various deformations effectively and discover the deformation regularity and trend of deformation body, people propose an indirect deformation prediction method. It applies the segmented model of multidimensional time series to present an inertial measurement parameters. The method applies the MIMU measurement unit to realize data collection. Through the analysis of filter compensation, it conducts the deformation prediction. MIMU signal changes on time rate and has high noise. According to the analysis of error sources of MIMU signal, we adopt the method of time series analysis to data preprocessing and establish the AR model of the time series. By using AR-Kalman filter algorithm, we take care of random error processing. Due to deformation occurs more slowly. It’s difficult for the deformation prediction of the time series data after filtering and MIMU directly calculats posture. So this paper through the calculation of similarity of each period of time series predicts the deformation tendency of deformation. Through the acquisition of the results of the actual data of the bridge deformation, this paper introduces the deformation prediction method is correct and effective, and it can be implemented to the deformation forecast in practice.