Comparison of Neural Network Architectures for Simultaneous Tracking and Navigation with LEO Satellites

Kozhaya, Sharbel E.; Haidar-Ahmad, Jamil A.; Abdallah, Ali A.; Kassas, Zaher M.; Saab, Samer S.

doi:10.33012/2021.18110

Cited by 22 publications

(8 citation statements)

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“…The importance of low-altitude satellites, which are used for purposes such as observation and exploration on Earth, is increasing over time. Satellites known as Low-Earth orbit (LEO) are reliable systems capable of meeting such requirements with sufficient accuracy [8][9][10][11][12][13]. In addition to all the other benefits, they have all the necessary features for positioning when GNSS satellites cannot be used effectively [9].…”

Section: Intorductionmentioning

confidence: 99%

Modeling and simulation of earth coverage of a low earth orbit (LEO) satellite

Bakırcı

2024

European Mechanical Science

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Efficient management of operations in near space, just beyond the Earth’s atmosphere, relies on the precise control of satellites positioned relatively close to our planet. Satellite systems, serving critical functions in telecommunications, observation, exploration, and more, have demonstrated their prowess as a transformative technology, consistently delivering high-precision data over numerous years. Among satellite systems, Low Earth Orbit (LEO) technology is gaining prominence due to its advantages, including lower power requirements for transmission, reduced propagation delays, and heightened coverage for polar regions. Achieving optimal efficiency from LEO satellites necessitates a thorough understanding of their fundamental orbital parameters and precise control over them. This study explores the orbital analysis and Earth coverage considerations of LEO satellites, scrutinizing orbital parameters in detail to compute coverage areas across various scenarios. Through this investigation, the potential benefits of data exchange with ground stations facilitated by LEO satellites are explored. In addition, the implications are discussed regarding the adjustment of data exchange topologies according to geographical locations and country borders.

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Section: Intorductionmentioning

confidence: 99%

Modeling and simulation of earth coverage of a low earth orbit (LEO) satellite

Bakırcı

2024

European Mechanical Science

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“…This is beneficial to many end-users applications as it provides faster access to the required orbit solutions [145]. Machine learning for orbit determination of LEO satellites has been implemented by some studies [146][147][148][149]. In [146,149], Time delay neural network (TDNN) which is a type of Feed Forward Neural Network (FFNN), and LSTM are used for simultaneous tracking and navigation with LEO satellites.…”

Section: Leo Satellites: Orbit Determination and Positioningmentioning

confidence: 99%

“…Machine learning for orbit determination of LEO satellites has been implemented by some studies [146][147][148][149]. In [146,149], Time delay neural network (TDNN) which is a type of Feed Forward Neural Network (FFNN), and LSTM are used for simultaneous tracking and navigation with LEO satellites. A neural network is implemented in [147,148] to mitigate GNSS multipath for LEO positioning applications.…”

Section: Leo Satellites: Orbit Determination and Positioningmentioning

confidence: 99%

“…8 shows that 23.47% (50 of 213) of studies did a comparison between non-ML and ML models [14, 31, 40, 43-45, 51-53, 58-60, 73, 80, 93, 94, 103, 105, 107, 110, 112, 114, 126, 129-131, 138, 139, 143, 146, 159-167, 169-179]. Furthermore, 2.82% (6 of 213) of studies compared one or more non-ML model with one or more ML model [49,71,101,168,236,237], while 2.82% (6 of 213) of studies implemented a hybrid between ML and non-ML algorithm [82,118,149,[238][239][240]. These hybrid implementation studies claim their performance is better than ML only and non-ML only implementations.…”

Section: E ML Vs Non-ml Models (Rq4a)mentioning

confidence: 99%

“…For example, RF, SVM, CNN, and ANN have been investigated most extensively in academia, therefore, these can first be taken into consideration by practitioners, as a useful complement to the existing traditional GNSS model. However, because of the limited number of studies comparing ML models and traditional GNSS (non-ML) models found from this review, we recommend using both ML and non-ML models in parallel at the early stage of practice which has been shown to have promising accuracy by some studies [82,118,149,[238][239][240]. The replacement of the non-ML (traditional GNSS) models with ML models can only be considered when the ML model performs significantly and consistently better than the existing traditional GNSS model.…”

Section: Implications For Research and Practicementioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

Siemuri

Selvan

Välisuo

et al. 2022

IEEE Trans. Aerosp. Electron. Syst.

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In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. The aim of this research is to perform a systematic review of the type of ML models utilized in GNSS use cases, their performance with respect to accuracy, their comparison with other models (ML and non-ML), and their GNSS application context. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning

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