Error Characteristic Analysis and Filtering Algorithm for GNSS Time-Series Data

Hongli Zhang

doi:10.3390/s25020361

2025-01-09

DOI：10.3390/s25020361

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摘要

En 中文

Under regional environmental conditions such as open-pit mines and construction sites, there are usually fixed GNSS measurement points. Around these fixed stations, there are also mobile GNSS measurement modules. These mobile measurement modules offer advantages such as low power consumption, low cost, and large data volume. However, due to their low accuracy, these modules can only provide approximate positions as monitoring data, such as for vehicle management in open-pit mines. To extract more information from the existing large volume of low-accuracy data, it is necessary to process these low-accuracy data. Under conditions of the same time and space in a small area, factors affecting measurement accuracy can be comprehensively considered. By analyzing the temporal GNSS data within the same spatiotemporal small region and understanding the variation patterns of measurement errors, a general equation for measurement error variation can be formulated. Using filtering methods, the data quality can be improved. Through the analysis of the experimental data in this study, it was found that the variation patterns of measurement data obtained by devices of the same accuracy during the same time period are generally consistent. After applying filtering methods, the measurement accuracy of each station improved by up to approximately 95.9%, with a minimum improvement of approximately 84.4%. Under the condition of a 95% confidence level, the reliability increased by up to approximately 73.2%, with a minimum improvement of approximately 58.2%. These experimental results fully demonstrate that under regional spatiotemporal conditions, the temporal data obtained by GNSS measurement devices with similar accuracy exhibit similar error distribution patterns. Applying the same filtering method can significantly improve the accuracy and reliability of measurement data.

Keyword:

time series data

filtering algorithm

error compensation