Integrity risk (IR) is a critical metric for evaluating the integrity performance of RAIM [15], and the computation methods can be classified into two groups. The first group calculates the ratio of the number of integrity failure samples to the total number of samples. This method can obtain an accurate integrity risk because there is no limit to the distribution of samples; however, it requires long-term observation to obtain large samples, which may be impractical when implementing a very small probability such as \(10^{-7}\). From the basic concept of integrity risk, the second group computes the probability of integrity failure by assuming a statistical distribution of samples such as Gaussian [16; 17]. Since the first group of methods requires a sufficient number of samples and it would be unacceptable if the effect of different parameters on integrity risk needed to be accounted for. In contrast, the Gaussian distribution-based method requires fewer samples and can intuitively quantify the relationship between integrity risk and its parameters. Therefore, we computed RAIM integrity risk based on the Gaussian distribution. Using the worst-case protection concept, a conservative estimation method of worst-case integrity risk (WIR) was developed to obtain the maximum integrity loss caused by the improved integrity parameters.  
Sensitivity analysis is an effective approach for studying the effect of the parameters on integrity risk. [PERSON] et al. [18] conducted a sensitivity analysis on the availability of advanced RAIM (ARAIM) using real-world data from monitoring stations in Australia, and he demonstrated that a combination of GPS and BDS significantly improved availability. To clarify the impact of the integrity support message (ISM) on ARAIM performance, [PERSON] et al. [19; 20] used a sensitivity analysis to prove that GPS constellation integrity parameters had a significant impact on ARAIM performance. A corresponding analysis was carried out to assess the impact of different integrity parameters on ARAIM performance with BDS in the Asia-Pacific region [21]. A sensitivity analysis of RAIM is still absent according to our best knowledge. Although the baseline concept in this contribution was motivated by [PERSON] et al. [19; 20], the sensitivity analysis of RAIM is still necessary because of its stronger autonomy and greater number of applications in the GNSS community relative to ARAIM. Moreover, the effect of integrity parameters on RAIM is different from ARAIM because the protection levels are constructed to deal with different integrity risk resources. Specifically, besides the threat of satellite failure, which is the focus of RAIM, ARAIM also needs to account for atmospheric anomalies because ARAIM aims to satisfy higher grades of RNP in civil aviation than RAIM. To achieve the robust result of sensitivity analysis, we conducted a RAIM integrity risk sensitivity analysis based on the proposed WIR method to restrict the integrity loss caused by the improved \(P_{\text{sat}}\) and \(URA\).  
A conservative integrity risk estimation method is proposed based on the worst-case protection concept. The sensitivity of worst-case integrity risk to \(P_{\text{sat}}\) and \(URA\) is analyzed. Acceptable \(P_{\text{sat}}\) and \(URA\) are provided for different integrity risk requirements. Finally, we analyze the simulation experiment results and summarize the research findings.