The Use of the Carrier to Noise Ratios (C/N<sub>0</sub>) to Alleviate the Problematic GNSS Phase Multipath

Madonsela, Bheki; Davidson, Innocent E.; Mukubwa, Emmanuel

doi:10.1109/saupec55179.2022.9730774

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…The UERE indicates the quality of satellite measurements (pseudo-range), and it is affected by the satellite signal power [33,34], which can be expressed by the value of signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) [34]. The value of SNR and CNR increases with the strength of a satellite signal [35]. Since the GNSS's positioning accuracy relies on GDOP and UERE, satellites should be selected based on them.…”

Section: Weighted Geometric Dilution Of Precision (Wgdop)mentioning

confidence: 99%

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Alluhaybi,

Psimoulis,

Remenyte-Prescott

2024

Remote Sensing

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Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.

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Section: Weighted Geometric Dilution Of Precision (Wgdop)mentioning

confidence: 99%

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

Alluhaybi,

Psimoulis,

Remenyte-Prescott

2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

The Use of the Carrier to Noise Ratios (C/N₀) to Alleviate the Problematic GNSS Phase Multipath

Cited by 1 publication

References 17 publications

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

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The Use of the Carrier to Noise Ratios (C/N0) to Alleviate the Problematic GNSS Phase Multipath

Cited by 1 publication

References 17 publications

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets

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The Use of the Carrier to Noise Ratios (C/N₀) to Alleviate the Problematic GNSS Phase Multipath