## 1 Introduction  
The real-time kinematic (RTK) positioning technique is a popular Global Navigation Satellite System (GNSS) based positioning technique. It is substantially different from the precise point positioning (PPP) technique that typically requires a long convergence time of tens of minutes [1; 2; 3],because the RTK positioning technique removes or mitigates observation errors such as clocks, hardware biases, and a large part of the spatially correlated errors such as the atmospheric delays. This enables ambiguity resolution within a short time. For the RTK of short baselines, e.g., less than 10 km, the atmospheric delays can be considered significantly reduced under quiet atmosphere conditions. The remaining biases mainly consist of the multipath effects. They could vary strongly with the measurement environments, for instance in urban areas, and thus may hamper successful ambiguity resolution and bias the positioning results. To de-weight the observations with large noise, multipath and atmospheric residuals, elevation-dependent weighting functions are typically used in the RTK positioning [4]. The elevation angle, however, is not the only factor that could be used to build a weighting model. As shown in [5; 6], the weighting model exploiting the carrier-to-noise density ratio (C/N\({}_{0}\)) can also effectively de-weight low-quality observations and improve the positioning results. [PERSON] et al. [7] also utilised C/N\({}_{0}\) to weight the code measurements in signal-degraded environments. In environments with complicated multipath effects and non-line-of-sight (NLOS) errors, e.g., in urban anyons, considering both the elevation angles and the C/N\({}_{0}\) in the weighting model was found to be useful for code-based positioning [8]. [PERSON] et al. [9] also used mixed elevation-based and C/N\({}_{0}\)-based model to weight the code noise and multipath in signal-degraded scenarios. Further studies were also performed with respect to the stochastic modelling of baseline data [10; 11; 12]. In RTK positioning, the multipath effects remaining in the double-differenced (DD) observations strongly vary with the measurement environments. This variation also differs for different observation types. For a weighting model considering both the elevation angles and the C/N\({}_{0}\), it is thus important to adjust the impacts of these two factors under different measurement scenarios and for different observation types. In this contribution, the first issue to be addressed is to develop a weighting model allowing different and adjustable impacts of the elevation angles and the C/N\({}_{0}\). The weighting coefficients of these two factors are searched for the open-sky and the suburban scenarios, under the ambiguity-float and -fixed modes. For the ambiguity-fixed case, a sequential exclusion algorithm is developed for ambiguity resolution, so that possibly many ambiguities at different elevation angles and C/N\({}_{0}\) can be resolved at each epoch, and the remaining residuals can be used for the determination of the weighting coefficients. The weighting models achieving the largest positional benefits are then used for further analysis. In road transport applications, integrity monitoring (IM) is an essential task to ensure that the positioning errors are less than the alert limit (AL) with a pre-defined probability of hazardous misleading information (PHMI). With the varying satellite geometry over time, the protection levels (PLs) are computed to bound possible positioning errors under the PHMI, and are compared with the AL. In case that the AL is exceeded, a warning message is sent to users within the time-to-alert. The IM was investigated for aviation over several decades [13; 14; 15; 16; 17]. In one IM approach, the solution separation test (SST) for the fault detection and exclusion (FDE) procedure is first applied. The protection levels are produced next within the advanced receiver autonomous integrity monitoring (ARAIM) algorithm for both the horizontal and vertical guidance [13; 14; 18].