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

Hongli Zhang1, Yijin Chen1, Kemeng Li1

  • 1College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.

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|January 25, 2025
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Summary
This summary is machine-generated.

This study enhances low-accuracy Global Navigation Satellite System (GNSS) data from mobile modules. Analyzing temporal error patterns and applying filtering methods significantly improves measurement accuracy and reliability for regional monitoring.

Keywords:
error compensationfiltering algorithmtime series data

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Area of Science:

  • Geomatics Engineering
  • Environmental Monitoring
  • Data Science

Background:

  • Fixed and mobile Global Navigation Satellite System (GNSS) modules are used in environments like open-pit mines.
  • Mobile GNSS modules offer benefits like low power and cost but have low accuracy, limiting their use to approximate positioning.
  • Processing large volumes of low-accuracy GNSS data is crucial for extracting valuable information.

Purpose of the Study:

  • To develop a method for improving the accuracy and reliability of low-accuracy mobile GNSS data.
  • To analyze temporal error variation patterns in GNSS measurements within specific spatiotemporal regions.
  • To formulate a general equation for measurement error variation and apply filtering techniques.

Main Methods:

  • Comprehensive analysis of factors affecting measurement accuracy in a defined spatiotemporal region.
  • Temporal analysis of GNSS data to identify measurement error variation patterns.
  • Application of filtering methods to improve data quality.

Main Results:

  • Measurement error patterns are consistent for devices of similar accuracy within the same time period and region.
  • Filtering methods improved measurement accuracy by 84.4% to 95.9%.
  • Reliability increased by 58.2% to 73.2% at a 95% confidence level.

Conclusions:

  • Temporal GNSS data from similar accuracy devices exhibit consistent error distribution patterns under regional spatiotemporal conditions.
  • The proposed filtering approach significantly enhances the accuracy and reliability of low-accuracy GNSS measurements.
  • This method enables more effective utilization of mobile GNSS data for applications like vehicle management in mining.