Research on the method of detecting and eliminating gross errors in Beidou monitoring time series

In deformation monitoring work, there are often gross errors in the monitoring data sequence. Due to the complex distribution of the monitoring data sequence, the conventional gross error detection method is not ideal in the processing of the monitoring data sequence. Based on this, the improved 3σ method based on wavelet analysis is proposed to achieve the purpose of identifying and eliminating gross errors. The experimental results show that the method can identify and eliminate the gross error signals that are randomly added, and it can fit the overall trend of the detail signal and the data signal well. The simulation example of the algorithm illustrates its trend extraction. The accuracy fully meets the requirements of gross error identification and elimination.