# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 3 Performance Evaluation
Similarly, the signal quality in the P3 and P4 windows exhibited similar characteristics, i.e., significant variations in the signal strength, and higher values of range residuals were highly correlated with variations in the GNSS fundamental quality indicators as shown in Figure 3.  
During the observation windows P1 and P4 in Figure 4b, the CNR values showed great constancy due to open-sky views with mostly direct LOS reception. The average CNR values during the P1 and P4 observation windows were found to be 41 and 45.3 dB, respectively. The range residuals for P1 and P4 were almost zero confirming the fact that the environmental context in which the receiver was operating in P1 and P4 was suitable for navigation. In the second observation window, i.e., P2, the signal quality degraded somewhat compared to P1 and P4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Satellites in View** & **PDOP** & **RMS Error** \\  & **max/avg/min** & **max/avg/min** & **max/avg/min** \\ \hline P1 & 37/34/28 & 1.17/0.85/0.75 & 2.4/1.3/1.0 \\ P2 & 35/27/17 & 2.21/1.24/0.84 & 7.8/4.1/1.4 \\ P3 & 36/27/10 & 4.21/1.32/0.80 & 14.3/5.3/1.24 \\ P4 & 38/34/27 & 1.29/0.97/0.79 & 1.9/1.3/1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of quad-constellation GNSS receiver performance under different types of environments denoted by the P1, P2, P3 and P4 observation windows. The CNR values deviated between 25.3 and 46.3 dB. These variations show a significant correlation with the range residuals, as the average and maximum range residuals were found to be 2.45 and 10.3 m, respectively, in Figure 4c. On comparing the range residuals for the observation window P2 with those of P1 and P4, we concluded that these values of range residuals can lead to significant errors in the positioning solution. Finally, the third observation window (i.e., P3) had the greatest effect on the quality of the received signals, which can be easily identified in Figure 4b,c in terms of the CNR and range residuals, respectively. Although the elevation angle for the P3 window is mostly above 30\({}^{\circ}\), there are rapid random fluctuations in the CNR with a minimum observed value of 23 dB and a maximum value of 48 dB leading to an average range residual of 3.25 m, which is significant when lane-level accuracy is required. The field experimentation and performance evaluation of multi-constellation and multi-frequency GNSS indicates that, despite remarkable advancements in GNSS-based navigation technologies, standard GNSS services still cannot achieve the required navigation performance across all kinds of environmental contexts. In fact, it is evident from the performance evaluation results that the GNSS receiver experienced severe inaccuracies when navigating from open-sky views to dense urban areas. Hence, the deployment of additional mechanisms to minimize the inaccuracies is inevitable in order to meet the required navigation performance in such environments. Although there has been substantial research into improving GNSS performance in urban environments by detecting, modeling, de-weighting and mitigating the NLOS/multipath effects at various levels, such as the antenna, receiver and measurement or position levels. These techniques work fairly well in certain environments but are of limited effectiveness in others as discussed in detail in Section 1. Hence, after the performance evaluation of a GNSS receiver in the different environments above, we concluded that, in order to operate reliably and effectively in a wide range of environments, a GNSS receiver is required to adopt different mitigation techniques according to the detected environmental context. This requires the design and implementation of a model that is adaptive in nature, can accurately detect and identify working environments and can then adjust the mitigation strategy accordingly. Keeping this in mind, in the next section, a new context-aware navigation (CAN) model is presented that works on an adaptive mitigation strategy based on the detected environment.