In Global Navigation Satellite System (GNSS) positioning, observation precisions are frequently impacted by the site-specific unmodeled errors, especially for the code observations that are widely used by smart phones and vehicles in urban environments. The site-specific unmodeled errors mainly refer to the multipath and other space loss caused by the signal propagation (e.g., non-line-of-sight reception). As usual, the observation precisions are estimated by the weighting function in a stochastic model. Only once the realistic weighting function is applied can we obtain the precise positioning results. Unfortunately, the existing weighting schemes do not fully take these site-specific unmodeled effects into account. Specifically, the traditional weighting models indirectly and partly reflect, or even simply ignore, these unmodeled effects. In this paper, we propose a real-time adaptive weighting model to mitigate the site-specific unmodeled errors of code observations. This unmodeled-error-weighted model takes full advantages of satellite elevation angle and carrier-to-noise power density ratio (C/N0). In detail, elevation is taken as a fundamental part of the proposed model, then C/N0 is applied to estimate the precision of site-specific unmodeled errors. The principle of the second part is that the measured C/N0 will deviate from the nominal values when the signal distortions are severe. Specifically, the template functions of C/N0 and its precision, which can estimate the nominal values, are applied to adaptively adjust the precision of site-specific unmodeled errors. The proposed method is tested in single-point positioning (SPP) and code real-time differenced (RTD) positioning by static and kinematic datasets. Results indicate that the adaptive model is superior to the equal-weight, elevation and C/N0 models. Compared with these traditional approaches, the accuracy of SPP and RTD solutions are improved by 35.1% and 17.6% on average in the dense high-rise building group, as well as 11.4% and 11.9% on average in the urban-forested area. This demonstrates the benefit to code-based positioning brought by a real-time adaptive weighting model as it can mitigate the impacts of site-specific unmodeled errors and improve the positioning accuracy.

Outliers and multipath effects significantly affect the positioning accuracy and reliability of the global navigation satellite system (GNSS), which must be handled properly in real-time kinematic (RTK) positioning, especially for low-cost devices. In this study, an extended robust estimation method (EIGG3) considering the multipath effects is proposed for RTK positioning. The carrier-to-noise power density ratio ( $C/N0$ ) template function is introduced, and then, an extended shrinking factor of the bifactor equivalent weight is proposed, where one five-segment processing strategy considering the multipath effects is used. Two dedicated experiments, including one static experiment and one kinematic vehicle experiment, were carried out to evaluate the performance of the EIGG3 method in terms of positioning availability, accuracy, and ambiguity resolution. For the static experiment, the improved percentage of the EIGG3 method is 62.7%, 61.6%, and 52.1% compared to the traditional robust estimation method for the positioning accuracy in the $E$ -, $N$ -, and $U$ -directions, respectively. Also, the EIGG3 method can further improve the performance of ambiguity resolution. For the kinematic vehicle experiment, the EIGG3 method 15.4% improvements for positioning accuracy in an urban environment, and even 23.9% improvements can be obtained in some typical scenarios.