Combined PPP Relative Baseline Positioning Techniques for CORS Network Adjustment and Alignment to the NSRS Proceedings of the 38th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2025)

This study develops and evaluates least squares adjustments of simulated GNSS-derived coordinates from Precise Point Positioning (PPP) and relative baseline processing techniques. This method uses PPP observations to constrain absolute positions, while baseline observations constrain between-station (i.e., relative) geometry. The simulated configurations use a Monte Carlo approach to iteratively vary observation noise,. Positions derived from GIPSY-X PPP processing were analyzed and used to tune the simulated environment to realistic conditions, while baseline observation noise was derived from literature. Performance was quantified using formal position errors defined by the post-adjustment covariance matrix and by differences between simulated and estimated station positions. A robust weighting strategy was developed to model observation noise using measurement residuals from preliminary adjustments. Statistical tests were used to detect and reduce observation bias effects. Network geometries were configured to demonstrate weak and strong designs that either emphasize or reduce unmodeled noise or bias propagation effects. Positioning performance of combined coordinates are highly dependent on the noise properties or uncertainties of the initial input coordinates and stochastic modeling. The least-squares adjustment is most sensitive to baseline noise properties with max 3D errors ranging from 4mm to 700mm depending on the stochastic model. PPP noise properties have minimal impact on the combined coordinate estimation with sub-mm 3D errors regardless of stochastic model. Grant no. NA23NOS40000333