A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

Du, Huazheng; Hong, Yunqing; Xia, Na; Zhang, Guofu; Yu, Yi; Zhang, Jiwen

doi:10.1109/access.2020.3023244

Cited by 7 publications

(11 citation statements)

References 48 publications

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“…Various satellite selection methods have been developed since as early as the 1980s [26][27][28][29][30]. Most of them are carried out in the motivation of optimizing the accuracy of a fixed-size subset.…”

Section: Instructionmentioning

confidence: 99%

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

et al. 2021

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Satellite selection is an effective way to overcome the challenges for the processing capability and channel limitation of the receivers due to superabundant satellites in view. The satellite selection strategies have been widely investigated to construct the subset with high accuracy but deserve further studies when applied to safety-critical applications such as the receiver autonomous integrity monitoring (RAIM) technique. In this study, the impacts of subset size on the accuracy and integrity of the subset and computation load are analyzed at first to confirm the importance of the satellite selection strategy for the RAIM process. Then the integrated performance impact of a single satellite on the current subset is evaluated according to the performance requirement of the flight phase. Subsequently, a performance-requirement-driven fast satellite selection algorithm is proposed based on the impact evaluation to construct a relatively small subset that satisfies the accuracy and integrity requirements. Comparison simulations show that the proposed algorithm can keep similar accuracy and better integrity performances than the geometric algorithm and the downdate algorithm when the subset size is fixed to 12, and can achieve an average 1.0 to 2.0 satellites smaller subset in the Lateral Navigation (LNAV) and approach procedures with vertical guidance (APV-I) horizontal requirement trial. Thus, it is suitable for real-time RAIM applications and low-cost navigation devices.

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Section: Instructionmentioning

confidence: 99%

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

et al. 2021

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“…However, due to the required matrix inversion, the TM can be time-consuming as it calculates the WGDOP value of all possible satellite subsets, especially if the number of available tracked GNSS satellites is large. To reduce this computational burden, various techniques including the closed-form solution [18,19], maximum volume [20], artificial neural network (ANN) [21], and optimisation algorithms [22,23] have been used to select optimal satellite subsets based on WGDOP.…”

Section: Introductionmentioning

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%

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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“…Simultaneously, research endeavors focused on incorporating additional parameters, such as Receiver Autonomous Integrity Monitoring (RAIM) [30][31][32] and a Weighted Geometric Dilution of Precision (WGDOP) system [33,34], among other considerations [35][36][37][38], are also on the rise.…”

Section: Introductionmentioning

confidence: 99%

Signal Occlusion-Resistant Satellite Selection for Global Navigation Applications Using Large-Scale LEO Constellations

Guo,

Wang,

Sun

2023

Remote Sensing

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With the continuous construction of large-scale Low Earth Orbit (LEO) constellations, their potential for Global Navigation Satellite System (GNSS) applications has been emphasized. This study aims to derive an optimal positioning configuration formula based on the ratio of high-elevation and low-elevation satellites, which can improve the positioning accuracy and overcome the accuracy loss due to signal occlusion. A genetic algorithm is used to solve the optimal positioning configuration problem for large-scale satellite selection. Through a simulation using Starlink satellites currently in orbit, it is verified that the traditional recursive algorithm cannot be applied to satellite selection for large-scale constellations. The proposed formula has a similar accuracy to the Quasi-Optimal algorithm when there is no signal occlusion and the satellites are uniformly selected. However, the accuracy of the latter deteriorates significantly under signal occlusion. Our algorithm can effectively overcome this problem. Moreover, we discuss the effect of different types of obstructions on the accuracy loss. We find that the Quasi-Optimal algorithm is more sensitive to a single large-angle obstruction than multiple small-angle obstructions. Our proposed formula can reduce the localization accuracy degradation caused by signal occlusions in both scenarios.

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A Navigation Satellites Selection Method Based on ACO With Polarized Feedback

Cited by 7 publications

References 48 publications

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

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

Signal Occlusion-Resistant Satellite Selection for Global Navigation Applications Using Large-Scale LEO Constellations

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