# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 3 Performance Evaluation
Among all the observation windows, P3 showed greater deterioration in the number of tracked satellites, PDOP values and the RMS error. Since the vehicle was continuously navigating and the GNSS antenna was experiencing surroundings with different obstruction levels, on average, 27 satellites were being tracked by the receiver, and this varied between 10 and 36 based on the obstruction level and multipath/NLOS signal reception during the entire P3 window as can be seen in Figure 3a. The average PDOP value in P3 was found to be 1.24 with rapid variations between 0.80 and 4.21. In Figure 3c, the maximum and average RMS errors were 14.3 and 5.3 m, respectively, which are large and may be problematic for the receiver to correctly navigate in many applications. The last case, i.e., the P4 observation window, exhibited similar characteristics to P1 in terms of locked satellites, DOP and RMS errors due to similar environmental dynamics. It should be noted that, in this study, GNSS was used in standalone mode without any correction services or augmentation system. The results of the field experiments for the observation windows P1, P2, P3 and P4 for different types of environments are given  
Figure 3: The performance evaluation of a standard GNSS receiver in quad-constellation mode in terms of fundamental quality indicators, i.e., (**a**) the satellite availability, (**b**) PDOP and (**c**) RMS error over the entire experimental route containing different operating environments denoted by P1, P2, P3 and P4. in Table 4, which gives a summarized overview of how the GNSS fundamental quality indicators can be affected while navigating through different environments encountering blockages, multipath environments and NLOS signal reception. The performance evaluation results of a multi-constellation multi-frequency GNSS receiver in Figure 3 show that the urban canyon imposes greater challenges to positioning and navigation systems, and special measures need to be adopted to improve the GNSS availability and accuracy. The quality of the received signals and the statistical characteristics may vary in changing environmental contexts, and these variations can easily be observed in the received signal power, range measurements (i.e., the path delay and phase difference) and frequency because these factors are the major contributors to the correlation curve between the received signal and receiver-generated replica. In this paper, the quality of the received signals is analyzed by the Carrier-to-Noise Ratio (CNR) and range residuals along with the elevation angle of satellites. The signal quality assessment tests performed for the aforementioned observation windows, i.e., P1, P2, P3 and P4, are shown in Figure 4. Figure 4a shows the elevation angles of satellites over the entire observation period of 80 min, Figure 4b represents the CNR, and Figure 4c shows the range residuals. Typically, CNR is influenced by the elevation angle, and therefore PRN: 13 was selected here for quality assessment with increasing elevation angle trends during the observation periods. The signal characteristics in each observation window (i.e., P1, P2, P3 and P4) are highly correlated with the variations in fundamental quality indicators shown in Figure 3.  
Both the GNSS fundamental quality indicators and quality of signal are equally influenced by the type of environment. In the P1 and P4 observation windows, the excellent signal strength (i.e., CNR) with great constancy and minimal range residuals corresponds with better GNSS performance, i.e., adequate satellite availability, better DOP values and smaller RMS error due to open-sky views.