Abstract:We design a robust adaptive Kalman filter model to deal with the dynamic characteristics of rapid subsidence areas and the influence of gross errors in observation vectors on Kalman filter results. The model can identify two states of steady settlement and rapid settlement. Robust estimation is used to reduce the influence of gross errors in the observation vectors. In order to reduce the errors of the state model, an adaptive factor is used to adjust the dynamic model to improve the accuracy of the filtering results. This model is applied to the data processing of subsidence monitoring in a mining area for verification. Compared with the results of robust Kalman filter, the conclusion shows that the filtering model is better.
HE Han,TAO Tingye,FENG Jiaqi et al. Adaptive Robust Kalman Filtering Model and Its Application in Subsidence Area Monitoring[J]. jgg, 2019, 39(12): 1265-1269.