## 3 Results  
In order to analyze the performance of the robust extended Kalman filter algorithm, this subsection analyzes and verifies its performance by means of measured data. The data were collected from a monitoring station in the deformation monitoring area of a mining area in Inner Mongolia for a total of 3600 calendar elements (time corresponding to data), and measured data are collected from monitoring Station 1 in the deformation monitoring area of a ship lock in Hengxian County, Guangxi for a total of 3600 calendar elements. The set of data is solved using the robust EKF (\(k_{1}\) take 2.0, \(k_{2}\) take 3.0), and the results are shown in Figures 2-5. It can be seen that there are almost no outliers in the solved results after using the robust EKF. From the Figures 3 and 5, it can be seen that the ephemeris in which coarse differences appear in the conventional EKF are almost all free from coarse differences in the carrier phase double difference residuals after using the robust EKF, and nearly 95% of the coarse differences are suppressed or eliminated. As can be seen from the residual plots and the solved result plots, the coarse difference values appear in both horizontal and elevation aspects when the conventional EKF algorithm is used for parameter estimation, which seriously affects the overall accuracy of the solved results and cannot meet the needs of practical applications. When the robust EKF algorithm is used for processing, the coarse differences of both sets of measured data are effectively suppressed during the observation period, and the results are smoother. In terms of the coarse differences, compared to the conventional EKF algorithm, a robust EKF algorithm  
It is clear from the above experiments that the robust extended Kalman filter demonstrates effectiveness in coarse difference processing and illustrates the need for quality control of parameter estimation. The commonly used PAR algorithms for optimal subset selection strategies are usually based on satellite level, where satellites with poor accuracy are eliminated based on satellite elevation angle, signal-to-noise ratio, or the number of successive locking ephemerides. Compared with observation satellites with high elevation angles, low elevation angle observation satellites are more susceptible to interference factors, especially errors that cannot be weakened or eliminated by differential methods such as multi-path errors. In order to analyze and verify the effectiveness of the proposed algorithm, the measured data are selected for experimental validation. In this case, the monitoring area of Baise City, Guangxi Zhuang Autonomous Region, with a baseline distance of 75 km between reference stations, is selected, and the data is collected using a laboratory-developed BDS/GPS receiver with a total of 2735 calendar elements. The experimental data acquisition points are shown in Figure 6.  
Figure 4: Measured data 2, three-dimensional baseline results of traditional EKF. Figure 5: Measured data 2, three-dimensional baseline results of robust EKF. The full ambiguity resolution (FAR) algorithm, the conventional PAR algorithm, and the new algorithm proposed in this paper are used to solve this set of data, where the FAR algorithm only sets the cut-off angle of satellite elevation angle (set to 15\({}^{\circ}\) in this case), and satellites with elevation angles lower than this value do not participate in the construction of the double-difference equation. The conventional PAR algorithm uses a simple combination of satellite elevation angle and the number of consecutive locked calendar elements on the basis of the FAR algorithm to solve the fuzzy degree subset to filter and achieve partial ambiguity fixation. One of the relevant parameter settings is shown in the following Table 1.  
The ADOP threshold is set to 0.14 when the number of ambiguities is within the [6,10] interval, 0.135 when the number of ambiguities is within the interval [11,15], and 0.13 when the number of ambiguities is greater than 15.