# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 5 Conclusions  
GNSS signal characteristics have significant benefits in advancing modern industries. The integration of swarm intelligence and Industry 5.0 with GNSS makes the system more sustainable and resilient to provide seamless and precise positioning and navigation solutions. In this paper, the comprehensive assessment and quantification of GNSS signals under various environments due to multipath and NLOS is presented. For this purpose, field experimentation has been carried out at different candidate sites under static and dynamic configurations. A total of four experiments were performed. Three candidate sites were static, at the Department of Electrical Engineering, Sukkur IBA University, and one experiment is carried out at Sukkur, which is one of the most highly dense urban cities in Sindh, Pakistan, for the dynamic mode. These sites were pre-surveyed and carefully chosen to quantify the effects of MP/NLOS. The GNSS performance has a significant impact due to the environmental change, as when there are clear open sky views, there are minimal chances for degradation. As the environment becomes congested/complex, the degradation increased, which deteriorated the GNSS positioning and navigation performance. Based on detailed evaluation, the thesis proposed an RSQM algorithm to initially classify the signal quality into LOS and NLOS, and then, adaptive mitigation was applied accordingly. The RSQM algorithm dynamically adjusts receiver settings based on the detected signal quality to achieve optimal navigation performance. The authenticity of the RSQM algorithm was verified through a field experiment on the same route and similar environmental conditions as used without the RSQM algorithm. The mitigation results shows the improved navigation performance in terms of position error, which is reduced from 5.4 m to 2.3 m on average. In future work, the research can be extended to include the challenges of autonomous vehicles. Seamless, accurate and precise positioning and navigation solutions are needed for autonomy with accuracy levels within centimeters. Future research will be focused on meeting the requirements of autonomous vehicles with adaptive navigation through machine learning and deep learning algorithms. Conceptualization, H.M. and M.A.S. ; Data creation, A.H. and H.M.; Formal Analysis, A.H. and H.M.; Funding Acquisition, G.E.M.A., S.A.M. and W.-G.K.; Investigation, A.A.M. ; Methodology, H.M. and A.A.M. ; Project Administration, M.A.S., G.E.M.A. and S.A.M. ; Resources, A.A.M., S.A.M. and A.H.; Software, H.M. and S.A.M. ; Supervision, M.A.S. and A.A.M. ; Validation, A.A.M. and W.-G.K.; Visualization, A.H.; Writing--original draft, H.M. and G.E.M.A. ; Writing--review and editing, A.A.M. and A.H.; All authors have read and agreed to the revised version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) funded by Korean Government Ministry of Science and ICT (MSIT) under Grant RS-2022-00166977. The real-time GNSS data that is utilized in the paper is acquired through experimental setup explained in section field experimentation. This research commenced at the GNSS and Space Weather Laboratory, Department of Electrical Engineering, Sukkur IBA University, Pakistan, and subsequently received support from the Interdisciplinary Research Centre for Aviation and Space Exploration (IRCASE) at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. The authors express gratitude for the financial support provided by National Research Foundation of Korea (NRF) funded by Korean Government Ministry of Science.