## 2 Materials and Methods
### Literature Review and Significance of Research
#### 2.1.2 Review of GNSS Data Quality Assessment Methods  
GNSS data quality metrics include data utilization ratios, code multipath magnitudes, cycle slip occurrence rates, and signal-to-noise ratio (SNR) analysis. The authors of [24] used TEQC and Anubis software to analyze the data utilization, cycle slip ratio, and multipath of a CORS station in Guilin and compared the performance between the two tools. IGS provides typical daily data quality assessment results with TEQC for every IGS CORS, as shown in Figure 1. However, TEQC can only assess GPS, GLONASS, and Galileo (partly) data. G-Nut/Anubis can support most navigation systems, but its availability to users is limited by only offering a free version in Linux [25]. GFZRNX provides limited quality check metrics, which cannot be used for cycle slip detection and multipath estimation [26]; the tool mainly provides RINEX file editing functions.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Site ID** & **Average MP1 (m)** & **Average MP2 (m)** & **Receiver Type** & **Antenna Type** \\ \hline AIRA & 0.56 & 0.62 & \begin{tabular}{c} TRIMBLE \\ NETR9 \\ \end{tabular} & TRM59800.00 \\ \hline BAMF & 0.66 & 0.61 & SEPT POLARX5 & SEPTHOKE\_B3E6 \\ \hline GOL2 & 0.9 & 0.94 & \
\begin{tabular}{c} ASHTECH \\ UZ-12 \\ \end{tabular} & AOAD/M\_T \\ \hline JPLM & 0.23 & 0.23 & SEPT POLARX5 & AOAD/M\_T \\ \hline NRC1 & 0.41 & 0.44 &
\begin{tabular}{c} JAVAD \\ TRE\_G3 TH \\ DELTA \\ \end{tabular} & AOAD/M\_T \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of daily average MP1 and MP2 of five IGS tracking stations.  
Figure 1: _Cont._  
Among all these quality-checking metrics, multipath errors and noise should be the most important because they cannot be mitigated or eliminated like other GNSS biases and errors through differencing. The multipath effect is independent for each satellite and receiver and varies with time [14; 15]. With the increasing number of GNSS systems, researchers are studying the multipath effect on the multi-GNSS system. The authors of [27] (2016) investigated the measurement noise of the new GNSS signals. The authors of [28] conducted a comprehensive multipath analysis on both pseudorange and carrier-phase measurements of GPS, GLONASS, Galileo, and BeiDou. However, each observation session in the experiment was only 4 h, which did not cover the whole multipath pattern of a  
Figure 1: IGS daily data assessment results at LBCH station from 16 May to 16 August 2022. (**a**) Daily RINEX observations (no BDS and QZSS data); dotted line: expected, solid line: observed. (**b**) Cycle slips (GPS, BDS, and QZSS are always zero). (**c**) RMS multipath in meters (Galileo MP2, BDS, and QZSS are always zero). (**d**) RINEX latency in seconds (always zero). (**e**) Residuals in millimeters; green: north, purple: east, aqua: vertical. (Plots are downloaded from the IGS website; text/scale cannot be changed.)  
satellite/site. The authors of [29; 30; 31] studied the multipath effect at CORS and IGS stations and analyzed the sites and equipment with respect to the multipath effect. In this paper, a 60-day observation period was chosen to thoroughly investigate the multipath effect of GNSS satellites at the selected sites; most possible multipath errors at a site due to satellite orbiting, the sidereal day, and earth rotation are covered. In fact, one of the goals of multipath analysis is to check the data quality of GNSS stations in order to provide a better GNSS service or more information to the user. Since there are changes in satellite orbits, the site environment, and receiver/antenna hardware (including the health and stability of electronic components), data quality assessment should be carried out regularly (e.g., every year).