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Related Concept Videos

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Related Experiment Video

Updated: May 10, 2026

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Adaptive station selection incorporating observation data quality for UPD estimation.

Shouzhou Gu1,2, Long Xiao3,4, Jinzhong Mi1,2

  • 1Chinese Academy of Surveying and Mapping, Beijing, 100036, China.

Scientific Reports
|May 8, 2026
PubMed
Summary

This study introduces a new method for selecting multi-Global Navigation Satellite System (multi-GNSS) stations to improve uncalibrated phase delay (UPD) estimation. The approach enhances data quality and reduces computation time by adaptively choosing optimal stations.

Keywords:
Adaptive station selectionDempster-Shafer (D-S) evidence theoryDynamic gridMarginal benefitObservation data qualityPosition dilution of precision (PDOP)Uncalibrated phase delay (UPD)

Related Experiment Videos

Last Updated: May 10, 2026

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

Area of Science:

  • Geodesy and Satellite Navigation
  • Signal Processing

Background:

  • Multi-GNSS Experiment (MGEX) stations exhibit uneven spatial distribution and variable data quality.
  • Traditional uncalibrated phase delay (UPD) estimation methods often overlook station geometry and data quality, limiting accuracy and efficiency.

Purpose of the Study:

  • To develop an adaptive station selection method (CAS) that integrates observation data quality for improved UPD estimation.
  • To overcome the limitations of existing methods by considering both spatial distribution and data quality.

Main Methods:

  • Developed a position dilution of precision (PDOP) and UPD error propagation model.
  • Utilized marginal benefit theory to determine the optimal number of stations.
  • Established a multi-indicator evaluation system using Dempster-Shafer (D-S) evidence theory for data quality assessment.
  • Implemented a dynamic grid algorithm balancing spatial geometry and data quality.

Main Results:

  • The proposed CAS method selected 80 optimal stations (30% of global stations) for BeiDou-3 Navigation Satellite System (BDS-3) data.
  • Estimated Narrow-Lane (NL) UPD products achieved accuracy better than 0.05 cycles.
  • Discrepancy with full-station solutions was less than 0.002 cycles, demonstrating comparable precision.
  • Computational time was reduced by 54.1%.

Conclusions:

  • The CAS method effectively addresses spatial distribution and data quality issues in MGEX station selection.
  • Achieved high-accuracy UPD products with significantly reduced computational cost.
  • Provides a robust framework for optimizing station selection in GNSS data processing.