# 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 detailed analysis of an on-ground GNSS receiver, characteristics of the received signals and real-time context detection indicate that the severity and nature of degradation is inherently dependent on the type of environment, which is responsible not only for signal degradation but also for MP/NLOS signal reception. Hence, a context detection mitigation strategy can be an effective approach for mitigating the multipath effects. In this  
Figure 7: Environment context detection and categorization using CAN based on \(nSV_{avg}\) and \(CF\) parameter values. (**a**) Satellites in view, (**b**) Change Factor (CF) and (**c**) Environment Characterization. regard, CAN can be a useful tool that can be employed inside a GNSS receiver to counter multipath effects by adopting an adaptive mitigation strategy based on context detection. In this section, the utilization of CAN to mitigate multipath effects for improving the positioning accuracy is demonstrated. The environment categories in CAN, i.e., demand, degraded and highly degraded, are the indication of levels of deterioration effects, i.e., blockage, MP and/or NLOS. The mitigation strategy adopted here will be initiated for degraded and highly degraded cases only because there are minimal chances of performance degradation in a standard environment. Typically, in a degraded environment, the impact of blockage is found to be minimal because there is always a sufficient number of satellites (i.e., >15) that a GNSS receiver can acquire and track, and this can be verified by analyzing the P2 and P3 windows in Figures 3a and 7c, respectively. However, in such environments, the multipath/NLOS reception remains the major contributor to inaccuracies because the overall positioning solution depends on the number of tracked satellites and the quality of the received signals. Therefore, the proposed CAN method leverages the redundancy of range measurements when using multi-constellations because it provides the user with a much greater choice of signals, and thus the accuracy can be improved by selecting only those signals that are least contaminated by multipath environments and NLOS to improve the positioning accuracy. There exist several studies that have demonstrated the improvements in the positioning accuracy by detecting, de-weighting or excluding faulty range measurements [29; 55; 56; 57; 58; 59; 60]. Conventionally, sequential monitoring is used to rank and discard the affected measurements based on range residuals, the elevation angle and the CNR level. However, in the case of multi-constellation mode, individually monitoring a huge number of satellites with several parameters will result in a greater processing load and may cause a delay in decision making. Keeping this limitation in-view, CAN uses an adaptive approach based on the detected environment instead of individually monitoring and discarding the satellites. The signal strength or CNR level is a clear indication of the signal reception conditions, i.e., LOS or MP/NLOS reception. Typically, the satellites with high CNR values result in more accurate positioning solutions because these are less likely to be affected by MP/NLOS. In GNSS receivers, the CNR masks can be used to improve the accuracy by selecting signals with high CNR and excluding signals that have been attenuated due to reflection or diffraction. In the default settings, the CNR mask level is kept as low as 10 dB as shown in Table 5; however, when a degraded environment is detected by CAN, the CNR level is increased to 35 dB in order to discard the MP-affected signals. On the other-hand, the mitigation strategy adopted by CAN completely changes in the case of a highly degraded environment. Instead of discarding the satellites affected by MP, CAN updates the delay locked loop (DLL) bandwidth to deal with wider dynamics.