ABSTRACT This study focuses on the critical challenge of selecting an overall optimal intersystem bias (ISB) stochastic model to improve GNSS precise point positioning (PPP) performance. To address the limitations of prior studies that primarily focus on positioning accuracy and convergence time, this study establishes a comprehensive assessment framework integrating positioning accuracy, convergence time, ISB characteristics, residual analysis, correlation metrics and physical mechanism interpretation. Using 1‐month observational data from 140 International GNSS Service (IGS) stations, the results show that modelling ISB as a constant achieves the overall best PPP performance in terms of model simplicity, positioning accuracy, convergence speed and ISB sequence characteristics. Notably, even without ISB estimation, its effects are entirely absorbed by other parameters and do not propagate into pseudorange or carrier phase residuals, different from some previous findings. By integrating mathematical derivations, physical essence analyses and large‐sample statistical validations, this study innovatively proposes and quantitatively validates the novel theoretical concept of “spurious correlation”, providing a robust new perspective for interpreting parameter correlations and avoiding misinterpretation of isolated anomalies. Comparative data analysis from different periods (2019 and 2025) reveals that ISB temporal variations are inherently driven by the simultaneous visible satellite counts, simultaneous observation duration and position dilution of precision (PDOP). Furthermore, empirical results from three independent methods indicate that ISB exhibits a strong correlation with clock errors, a moderate‐to‐strong correlation with ambiguity parameters, a weak‐to‐moderate correlation with ionospheric parameters and no correlation with tropospheric parameters.
Ban Zhao (Thu,) studied this question.