Performance Evaluations of an Equatorial GPS Amplitude Scintillation Detector Using a Machine Learning Algorithm

Jiao, Yu; Hall, John J.; Morton, Y. Jade

doi:10.33012/2016.14554

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…In this paper, the manual annotation is considered as the reference ground truth for the detection performance analysis. The same approach has been used also in other works relying on machine learning for scintillation detection [17], [18].…”

Section: A Description Of Traditional Methodsmentioning

confidence: 99%

“…However, this approach relies on external data sources and instruments, which are not always available. A technique based on supervised machine learning Support Vector Machine (SVM) algorithm has been proposed for amplitude scintillation detection in [17] and [18]. This method has been extended to phase scintillation detection in [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

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Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

Linty

Farasin

Favenza

et al. 2019

IEEE Trans. Aerosp. Electron. Syst.

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This paper proposes a methodology for automatic, accurate, and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz postcorrelation data provided by a global navigation satellite system receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts.

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Section: A Description Of Traditional Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

Linty

Farasin

Favenza

et al. 2019

IEEE Trans. Aerosp. Electron. Syst.

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“…A clear strategy envisages the use of high‐level observables, including the amplitude‐ and phase‐scintillation indices, the signal CN0, and the satellites' elevation and azimuth. This was first done exploiting SVM for the detection of amplitude scintillation in Jiao, Hall, and Morton (2016) and Jiao, Hall, and Morton (2017a). The algorithm was trained using a large amount of real scintillation data, manually labelled, and showed detection accuracy in the range 91‐96%, outperforming other triggering systems analyzed.…”

Section: Scintillation Detectionmentioning

confidence: 99%

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

et al. 2020

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Ionospheric scintillation is the physical phenomena affecting radio waves coming from the space through the ionosphere. Such disturbance is caused by ionospheric electron‐density irregularities and is a major threat in Global Navigation Satellite Systems (GNSS). From a signal‐processing perspective, scintillation is one of the most challenging propagation scenarios, particularly affecting high‐precision GNSS receivers and safety critical applications where accuracy, availability, continuity, and integrity are mandatory. Under scintillation, GNSS signals are affected by amplitude and phase variations, which mainly compromise the synchronization stage of the receiver. To counteract these effects, one must resort to advanced signal‐processing techniques such as adaptive/robust methods, machine learning, or parameter estimation. This contribution reviews the signal‐processing landscape in GNSS receivers, with emphasis on different detection, monitoring, and mitigation problems. New results using real data are provided to support the discussion. To conclude, future perspectives of interest to the GNSS community are discussed.

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“…A technique based on the SVM algorithm has been suggested for amplitude scintillation detection [148], [149], and then later expanded to phase scintillation detection [150], [151].…”

Section: F Ionospheric Scintillation Detectingmentioning

confidence: 99%

Artificial Intelligence for Satellite Communication: A Review

Fourati,

Alouini

2021

Preprint

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Satellite communication offers the prospect of service continuity over uncovered and under-covered areas, service ubiquity, and service scalability. However, several challenges must first be addressed to realize these benefits, as the resource management, network control, network security, spectrum management, and energy usage of satellite networks are more challenging than that of terrestrial networks. Meanwhile, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has been steadily growing as a research field and has shown successful results in diverse applications, including wireless communication. In particular, the application of AI to a wide variety of satellite communication aspects have demonstrated excellent potential, including beam-hopping, anti-jamming, network traffic forecasting, channel modeling, telemetry mining, ionospheric scintillation detecting, interference managing, remote sensing, behavior modeling, space-air-ground integrating, and energy managing. This work thus provides a general overview of AI, its diverse sub-fields, and its state-ofthe-art algorithms. Several challenges facing diverse aspects of satellite communication systems are then discussed, and their proposed and potential AI-based solutions are presented. Finally, an outlook of field is drawn, and future steps are suggested.

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Performance Evaluations of an Equatorial GPS Amplitude Scintillation Detector Using a Machine Learning Algorithm

Cited by 10 publications

References 6 publications

Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

Survey on signal processing for GNSS under ionospheric scintillation: Detection, monitoring, and mitigation

Artificial Intelligence for Satellite Communication: A Review

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