A Navigation Satellite Selection Method Based on optimized DPSO Algorithm

Hong, Yunqing; Xu, Si Yu

doi:10.1109/bigdatasecurity-hpsc-ids49724.2020.00040

Cited by 1 publication

(3 citation statements)

References 15 publications

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“…Even though GNSS satellite selection is a discrete problem [26], not all of the five optimization algorithms are designed for this type of problem. The five optimization algorithms follow an iterative search process to find the optimal satellite subset.…”

Section: Methodology Of Selecting Tracked Gnss Satellites Based On Op...mentioning

confidence: 99%

“…However, only an integer is accepted in the satellite selection problem. This issue can be overcome by changing the algorithm design for a discrete problem [26,55,56] or simply rounding solution values [55,57]. To reduce complexity, the generated solutions from ABC and PSO were rounded to the nearest integer in each iteration.…”

Section: Methodology Of Selecting Tracked Gnss Satellites Based On Op...mentioning

confidence: 99%

“…However, it cannot reduce the number of calculations, so it becomes time-consuming when the number of satellites increases [25]. The maximum volume method is based on maximizing the volume of the tetrahedron or orthogonal projection created between the satellites [22,26]. The WGDOP value decreases (improves) as the tetrahedron volume and maximum orthogonal projection increase.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodology Of Selecting Tracked Gnss Satellites Based On Op...mentioning

confidence: 99%

Section: Methodology Of Selecting Tracked Gnss Satellites Based On Op...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

A Navigation Satellite Selection Method Based on optimized DPSO Algorithm

Cited by 1 publication

References 15 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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