## 5 Impacts of the Microwave Absorption: Observation Results  
_Multi-GNSS Code Multipath_  
The multipath effect is present in both the code and carrier phases of GNSS measurements; however, the code multipath is substantially larger and more variable between the receiver and antenna types. For the single point positioning technique, the multipath error is a significant contributor to the accuracy of the code measurements. For proper observation weighting, the understanding of the multipath effect and code noise can be useful [58]. Additionally, this data may reveal unique features of the receiver or information about the station's surroundings. Standard dual-frequency precise point positioning (PPP) solution performance depends on the pseudorange observable and code multipath error quality. Consequently this affects the time it takes to fix the float ambiguity to an integer. Furthermore, the presence of cycle slips contributes to resolving integer ambiguities and multipath estimation. In this study, we computed the code multipath on the pseudorange, \(MP_{k}\) for all frequencies and for all accessible signals, including multi-GNSS that provides dual-frequency observations in a way that allows the code multipath to be isolated. Since this technique is  
Figure 4: The photograph of KBG1 station’s Trimble (TRM159800.00) geodetic Choke Ring GNSS antenna integrated with SCIS radome (**left**) and the Eccosorb AN-W-79 (microwave absorber) material that surrounds station KBG1 (**middle**) and KBG2 antenna, Trimble (TRM159800.00) geodetic Choke Ring GNSS antenna integrated with an SCIS radome (**right**). Figure 3: Autocad drawing of GNSS station KBG1 to the (**left**) and KBG2 to the (**right**). All the values are given in millimetres. computationally simple, it has been widely used to measure total code multipath and noise on frequency \(k\) as follows:  
\[MP_{k}=P_{k}-\frac{(f_{i}^{2}+f_{k}^{2})}{(f_{i}^{2}-f_{j}^{2})}\frac{f_{i}^{2}}{ f_{k}^{2}}L_{i}+\frac{(f_{i}^{2}+f_{k}^{2})}{(f_{i}^{2}-f_{j}^{2})}\frac{f_{j}^{2}}{ f_{k}^{2}}L_{j}+C_{avg} \tag{7}\]  
where \(i\), \(j\) and \(k\) are frequency indices, \(L_{i}\) and \(L_{j}\) are carrier phase observables for frequencies \(f_{i}\) and \(f_{j}\) for the same satellite (\(i\
eq j\)) and \(C_{avg}\) is the bias, i.e., constant hardware delays and constant ambiguity. For a given satellite and receiver pair, the bias corresponds to fixed numbers across the whole track. The \(C_{avg}\) is computed using a moving average technique, i.e., a moving average window of \(MP_{k}\) estimates over a sequence of consecutive epochs of a specified duration. When estimating Equation (7), we implicitly disregard multipath from carrier phase observables, that are often two orders of magnitude smaller than the code multipath [23]. For modernized and new GNSS constellations with multi-frequency signals, a number of code multipath combinations are possible. We form \(GPSM1C\), \(GPSM1X\), \(GPSM2W\), \(GPSM2X\) and \(GPSM5X\) for GPS; \(GLOM1C\), \(GLOM1P\), \(GLOM2C\) and \(GLOM3X\) for GLONASS; \(GALM1X\), \(GALM5X\), \(GALM6X\), \(GALM7X\), and \(GALM8X\) for Galileo; and \(BDSM1X\), \(BDSM2I\), \(BDSM5X\), \(BDSM6I\), and \(BDSM7I\) for BeiDou. For the detailed signal observation type, frequency and tracking channel numbering, see ([[https://files.igs.org/pub/data/format/rinex304.pdf](https://files.igs.org/pub/data/format/rinex304.pdf)]([https://files.igs.org/pub/data/format/rinex304.pdf](https://files.igs.org/pub/data/format/rinex304.pdf)), accessed on 20 December 2021). We estimated the multipath root-mean-square (RMS) using the Anubis [58] software package over a sequence of 30 s epochs using all the linear combinations of available frequency bands for each GNSS constellation using Equation (7).