# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 4 Context-Aware Navigation (CAN)
### Mitigating Multipath/NLOS Effects in Different Environmental Contexts
The receiver settings used by CAN for mitigating multipath effects for different types of environments are shown in Table 5. The standard mode parameters are the default settings used for tracking the incoming signals. For a degraded environment, only the CNR masking level is increased whereas, for the highly degraded case, the CNR mask level is kept at the default value while the DLL bandwidth is increased. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Receiver Settings**} & \multicolumn{3}{c}{**Environmental Contexts**} \\ \cline{2-4}  & **Standard** & **Degraded** & **Highly Degraded** \\ \hline DLL Bandwidth (Hz) & 0.25 & 0.25 & 01 \\ CNR Mask (dB-Hz) & 10 & 35 & 10 \\ Elevation Mask & 10\({}^{\circ}\) & 10\({}^{\circ}\) & 10\({}^{\circ}\) \\ PLL Bandwidth (Hz) & 15 & 15 & 15 \\ Data Rate (Hz) & 01 & 01 & 01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The GNSS receiver level settings in three different operating contexts. In previous works, the fixed CNR masks or elevation masks were used for the detection and exclusion of faulty measurements, such as signals affected by MP or NLOS, to improve the positioning accuracy. These mitigation models or techniques, however, work effectively only in some contexts and not in all operating environments. The adaptive design of the proposed CAN allows the receiver to adaptively mitigate the effects of multipath or NLOS by updating the receiver tracking-loop parameters based on the inferred environment. The performance of a GNSS receiver in quad-constellation mode (i.e., GPS, GLONASS, Galileo and BeiDou) in terms of fundamental quality indicators with CAN enabled are shown in Figure 8. Figure 8a shows the number of tracked satellites, Figure 8b represents the position dilution of precision (PDOP), while the RMS error over the entire observation period is shown in Figure 8c. The results in Figure 8 show that almost the same level of performance was achieved in the observation windows P1 and P4 (standard environment cases) when compared with the results of the field experiments performed in Figure 3 without using CAN. This is clear because the CAN algorithm does not initiate any mitigation strategy when working in standard environments as explained above. On the other hand, in P2 observation window, the average number of satellites dropped to 22 in Figure 8a, which were 27 in Figure 3a without using CAN. In the P2 observation window, the environment was mostly identified by CAN as degraded, and thus the dropping of satellites is because of setting a higher CNR mask level and, thus, discarding the possible multipath/NLOS measurements in order to avoid them being used in the positioning solution. The effects of excluding the contaminated measurements in P2 using CAN can be seen in Figure 8c, where the average RMS error dropped to 3.61 m, which was 4.14 m without using CAN in Figure 3c. On the other-hand, the PDOP values in Figure 8b increased slightly with an average of 2.17, which was 1.24 without using CAN in the same observation window. Relying on PDOP alone is not a good indicator to check the efficacy of a positioning solution because, when there are multipath measurements, a good PDOP value may be attained along with significant errors in the positioning solution. This is what we experienced after applying CAN and, thus, excluding the multipath/NLOS measurements in the P2 observation window leading to increased PDOP values but with improved positioning accuracy. In the P2 observation window, the sole purpose of excluding the faulty measurements is to improve the positioning accuracy, which was successfully achieved after initiating CAN. In observation window P3, the environment is a blend of both the degraded and highly degraded environments due to rapid variations in the environmental context as a result of the moving vehicle.