# On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection
## 5 Conclusions and Recommendations  
The GNSS signal characteristics provide advantages in identifying and characterizing the environmental context to improve the positioning accuracy. This paper reinforced the fact that multi-constellation GNSS performance varies greatly with the environmental context and is highly likely to deteriorate in dense urban environments surrounded by high-rise buildings, congested roads, flyovers and under-passes. A performance evaluation  
Figure 10: GNSS accuracy achieved in a highly degraded environment (P3) with and without CAN. Figure 9: GNSS performance in terms of the RMS error at P3 with and without CAN. field study in these complex environmental scenarios revealed that a multi-constellation GNSS receiver, even after using four constellations, lacked consistency in maintaining the required accuracy thresholds across all kinds of environments. The significant deviations in the number of tracked satellites, geometric distribution of satellites (PDOP) and quality of the navigation signal in the degraded and highly degraded environments due to signal obstructions and NLOS/multipath effects resulted in erroneous, inconsistent and unreliable positioning solutions. To increase the capability of a multi-constellation GNSS receiver, the context-aware navigation (CAN) method effectively utilized the multi-constellation GNSS receiver measurement parameters to first identify the type of working environment and then initiated a mitigation strategy accordingly for optimal receiver performance. This was achieved by CAN through classifying the environment into three distinct categories: standard, degraded and highly degraded, based on two estimated features. After environment detection and characterization, CAN initiated the mitigation strategy by updating the CNR masking level in the case of a degraded environment or by changing the receiver tracking-loop parameters in the case of a highly degraded environment. The advantage of using CAN is its simplicity in terms of the implementation design, as it can be integrated in a GNSS receiver without great modifications. The efficacy of CAN was validated in this paper through field experimentation on the same route and similar environment dynamics as used for a GNSS receiver without using CAN in a quad-constellation mode. The real-time implementation of CAN navigating through the different types of environmental contexts showed that this algorithm reduced the signal degradation effects in degraded and highly degraded environments by improving the lane level accuracy, which was achieved 53% of the time when using a CAN-based GNSS receiver and was achieved only 32% of the time when not using CAN. In upcoming work, this study will be extended to larger context ranges, higher analysis windows, complex physical multipathways and concentrated quad-constellation conditions. Moreover, a more robust CAN algorithm using Deep-Markov inference will be designed to not only detect and classify an appropriate context but also to make autonomous variational decisions over the available set of GNSS signals. Furthermore, comparative analysis will be made between the offline and online compensation techniques for benchmark parameters, settings and configurations of the receiver under distinct dynamic environments. Conceptualization, A.H., A.A. and M.A.S. ; methodology, A.H. and M.A.S. ; software, A.H. and A.A.; validation, A.H., M.A.S. and S.K. ; formal analysis, S.K. ; investigation, A.H.; resources, M.A.S. and L.S. ; data curation, A.H.; writing--original draft preparation, A.H.; writing--review and editing, A.H., L.S. and H.M.; visualization, S.K., L.S. and H.M.; supervision, A.A.; project administration, M.A.S. ; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript. The authors wish to acknowledge the HEC (Higher Education Comission) of Pakistan for providing the financial support under National Research Program for Universities (NRPU) grant 6250/Sindh/NRPU/R&D/HEC/2016 for this research work. Not applicable.