Version 1
: Received: 10 October 2024 / Approved: 10 October 2024 / Online: 10 October 2024 (10:32:57 CEST)
How to cite:
Magsi, H.; Shah, M. A.; Abro, G. E. M.; Memon, S. A.; Hussain, A.; Memon, A. A.; Kim, W.-G. Adaptive Navigation Based on Multi-Agent RSQM Algorithm. Preprints2024, 2024100784. https://doi.org/10.20944/preprints202410.0784.v1
Magsi, H.; Shah, M. A.; Abro, G. E. M.; Memon, S. A.; Hussain, A.; Memon, A. A.; Kim, W.-G. Adaptive Navigation Based on Multi-Agent RSQM Algorithm. Preprints 2024, 2024100784. https://doi.org/10.20944/preprints202410.0784.v1
Magsi, H.; Shah, M. A.; Abro, G. E. M.; Memon, S. A.; Hussain, A.; Memon, A. A.; Kim, W.-G. Adaptive Navigation Based on Multi-Agent RSQM Algorithm. Preprints2024, 2024100784. https://doi.org/10.20944/preprints202410.0784.v1
APA Style
Magsi, H., Shah, M. A., Abro, G. E. M., Memon, S. A., Hussain, A., Memon, A. A., & Kim, W. G. (2024). Adaptive Navigation Based on Multi-Agent RSQM Algorithm. Preprints. https://doi.org/10.20944/preprints202410.0784.v1
Chicago/Turabian Style
Magsi, H., Abdul Aziz Memon and Wan-Gu Kim. 2024 "Adaptive Navigation Based on Multi-Agent RSQM Algorithm" Preprints. https://doi.org/10.20944/preprints202410.0784.v1
Abstract
In the era of industrial evolution satellites are being viewed as swarm intelligence that do not relies on single system but multiple constellations collaborate autonomously. This has enhanced the potential of the GNSS system to contribute in the improved position, navigation and timing (PNT) services. However, Multipath (MP) and Non-line-of-sight (NLOS) receptions remain the prominent vulnerability for GNSS in harsh environments. The aim of this research is to investigate its impact of MP and NLOS on GNSS performance and then proposed a Received Signal Quality Monitoring (RSQM) algorithm. RSQM works twofold: Initially it performs a signal quality test based on fuzzy inference system. The input parameters are Carrier-to-Noise ratio (CNR), Range Residuals (RR) and Code-Carrier Divergence (CCD) and it computes the membership functions based on mamdani method and classify the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements, while considering the satellite geometry constraints (i.e., DOP≤2).. For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configuration (GPS,BeiDou, GLONASS and Galileo) has been carried out in various operating environments. This RSQM based GNSS receiver has capability to identify signal quality and perform adaptive navigation accordingly to improve the navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4m to 2.3m averagely in the complex urban environment. Accumulating RSQM with GNSS has great potential for future industrial revolution (Industry 5.0) making things automatic and sustainable like autonomous vehicle operation.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
*
