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<p>To address the uneven spatial distribution and significant variations in observation data quality among multi-GNSS experiment (MGEX) stations, this paper proposes an adaptive station selection method (comprehensive adaptive site selection, CAS) for uncalibrated phase delay (UPD) estimation that incorporates observation data quality, thereby overcoming the limitations of traditional methods that neglect station geometry and data quality. A position dilution of precision (PDOP) and UPD error propagation model is developed. Using marginal benefit theory, the optimal number of stations is determined. A multi-indicator evaluation system based on Dempster-Shafer (D-S) evidence theory is established to assess data quality, enabling a dynamic grid algorithm that balances spatial geometry and data quality. The experiments are conducted using BeiDou‑3 navigation satellite system (BDS‑3) data. Experimental results demonstrate that the proposed method selects 80 optimal stations, accounting for only 30% of the global stations. The estimated Narrow-Lane (NL) UPD products achieve an accuracy better than 0.05 cycles, with a discrepancy of less than 0.002 cycles compared to the full-station solution, indicating comparable precision. Furthermore, the computational time is reduced by 54.1%.</p>

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Adaptive station selection incorporating observation data quality for UPD estimation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月8日

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  • Shouzhou Gu,
  • Long Xiao,
  • Jinzhong Mi,
  • Xuan Zhao,
  • Yidong Fan,
  • Bin Chu

摘要

To address the uneven spatial distribution and significant variations in observation data quality among multi-GNSS experiment (MGEX) stations, this paper proposes an adaptive station selection method (comprehensive adaptive site selection, CAS) for uncalibrated phase delay (UPD) estimation that incorporates observation data quality, thereby overcoming the limitations of traditional methods that neglect station geometry and data quality. A position dilution of precision (PDOP) and UPD error propagation model is developed. Using marginal benefit theory, the optimal number of stations is determined. A multi-indicator evaluation system based on Dempster-Shafer (D-S) evidence theory is established to assess data quality, enabling a dynamic grid algorithm that balances spatial geometry and data quality. The experiments are conducted using BeiDou‑3 navigation satellite system (BDS‑3) data. Experimental results demonstrate that the proposed method selects 80 optimal stations, accounting for only 30% of the global stations. The estimated Narrow-Lane (NL) UPD products achieve an accuracy better than 0.05 cycles, with a discrepancy of less than 0.002 cycles compared to the full-station solution, indicating comparable precision. Furthermore, the computational time is reduced by 54.1%.