A Comprehensive Evaluation Index for GNSS Observation Data Quality

Accurate assessment of Global Navigation Satellite System (GNSS) observation data quality is essential for ensuring the reliability of positioning and navigation applications.Traditional evaluation methods, which rely on single-index weighting or simplistic combinations of multiple indicators, have proven insufficient in capturing the multifaceted nature of observation quality.To address these limitations, a comprehensive evaluation framework was developed based on a combined weighting strategy that integrates the information entropy weight method and the coefficient of variation method.This hybrid approach enhances the objectivity and sensitivity of index weighting by leveraging the strengths of both methods.Furthermore, fuzzy mathematics theory was incorporated to model the uncertainty and vagueness inherent in GNSS observations, thereby enabling the systematic identification and exclusion of low-quality and low-confidence data.This integration allows for the robust evaluation of multi-constellation GNSS observation data, accommodating complex and variable observational environments.The proposed method was validated through empirical analysis, demonstrating superior performance in distinguishing high-quality data compared to conventional single-indicator and single-weighting approaches.Experimental results confirm that the proposed framework yields more reliable and scientifically grounded quality assessments, contributing to improved accuracy and stability in downstream GNSS applications.