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

Qing An; Lang Rao; Shusen Wu

doi:10.1117/12.2620302

11 November 2021 Research on the method of detecting and eliminating gross errors in Beidou monitoring time series

Qing An, Lang Rao, Shusen Wu

Proceedings Volume 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence; 120760M (2021) https://doi.org/10.1117/12.2620302
Event: Fourth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2021), 2021, Shanghai, China

Abstract

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.

Citation Download Citation

Qing An, Lang Rao, and Shusen Wu "Research on the method of detecting and eliminating gross errors in Beidou monitoring time series", Proc. SPIE 12076, 2021 International Conference on Image, Video Processing, and Artificial Intelligence, 120760M (11 November 2021); https://doi.org/10.1117/12.2620302

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