Galileo High Accuracy Service (HAS) Performance Analysis Report

    This report utilizes self-developed receivers from SinoGNSS for data collection. The data collection environment mainly in open sky. The tests was done over the globe, select some typical countries to analyse the performance in different location. It includes fours sites in China, and one site in Africa, South America, North America and Europe respectively. Multiple-station data undergoes continuous multi-day PPP testing for evaluation.

Fig.3 Test environment

The observation data used to evaluate the HAS service performance of the Galileo satellite navigation system come from data from 4 cities in China and 4 overseas countries. The specific site name distribution and the time of the samples displayed in the article are shown in Table 1.

Table 2 shows the processing methods of ambiguity, clock bias and other related parameters in the positioning process.

When using PPP-HAS for positioning, the basic observation equation of pseudorange and carrier wave is:

\( P_i = \rho_i + I_i + T + cdt_{r} - cdt^s - c(B_{r} + B^s) + \epsilon_{P} \)  (Eq.1)

\( \Phi_i = \rho_i - I_i + T + cdt_{r} - cdt^s - c(b_{r} + b^s) + \lambda_i N_i + \epsilon_{\Phi} \)  (Eq.2)

P represents the pseudo-range observation value, Φ represents the carrier observation value, λ represents the carrier phase wavelength, i represents that the error is related to the frequency of the signal, ρ represents the geometric distance between the satellite and the receiver, dt_r and dt^s are the receiver clock bias and satellite clock bias respectively, I represents the ionospheric delay, T represents the tropospheric delay, B_r, and B^s respectively represent the receiver-side hardware delay and satellite-side hardware delay of the pseudo-range part, b_r and b^s respectively represent the receiver-side hardware delay and satellite-side hardware of the carrier part, N is the integer ambiguity of the carrier phase signal in cycles, ξ represents observation noise and other errors not modeled. When performing PPP positioning, establish an ionosphere-free combined model according to Eq. 3 and Eq. 4.

\( P_{IF} = \frac{f_1^2 P_1 - f_2^2 P_2}{f_1^2 - f_2^2} = \rho + cdt_{r} - cdt^{s} + T + \epsilon_{P_{IF}} \)  (Eq.3)

\( \Phi_{IF} = \frac{f_1^2 \Phi_1 - f_2^2 \Phi_2}{f_1^2 - f_2^2} = \rho + cdt_{r} - cdt^{s} + T + \lambda_{IF} N_{IF} + \epsilon_{\Phi_{IF}} \)  (Eq.4)

Eq. 3 and Eq. 4 are the ionospheric-free combined observation equations. The ionospheric-free combined combination model can eliminate the influence of the first-order ionosphere. The parameters to be estimated only include 3 position parameters, receiver clock bias, zenith tropospheric delay and combined carrier phase ambiguity.  After establishing the model, domestic and foreign base stations are selected to receive satellite observation data and HAS data, and the relevant parameters are processed according to Table2. Fig.4 is used to verify the ionosphere-free precise positioning.

Fig.4 Precision single point positioning algorithm process based on PPP-HAS service

This section mainly uses PPP-HAS to evaluate positioning accuracy. Fig.5-Fig.12 show the PPP positioning deviations in four different cities, where the horizontal axis is GPS time. From the figure, we can see the positioning results of the site after the results converge. The domestic positioning results are statistically calculated and averaged to obtain the final result.

When the horizontal accuracy of the positioning results is within 20cm and the accuracy for 100 consecutive epochs does not exceed 20cm, the results are considered to have converged. Table 3 provides average statistics of the positioning results of four domestic sites.

By following you can get detail information on each station for the accuracy.

  Site 1:  Shanghai

Fig.5 PPP-HAS positioning solution of Shanghai site

Fig.6 Plane positioning map of Shanghai site

 

  Site 2:  Beijing

Fig.7 PPP-HAS positioning solution of Beijing site

Fig.8 Plane positioning map of Beijing site

 

  Site 3:  Xinjiang province

Fig.9 PPP-HAS positioning solution of Xinjiang site

Fig.10 Plane positioning map of Xinjiang site

 

  Site 4:  Guangzhou province

Fig.11 PPP-HAS positioning solution of Guangzhou site

Fig.12 Plane positioning map of Guangzhou site