Is this project an undergraduate, graduate, or faculty project?

Undergraduate

group

Poster Session; 5-minute Oral Presentation; 10-minute Oral Presentation

Authors' Class Standing

Danayit Mekonnen, Sophomore Lucas Tijerina, Senior Nicolas Gachancipa, Junior Julian Herrera, Senior Marissa Priore, Senior Daniel Nigro, Junior Devarshi Patel, Junior Pralay Vaggu, Graduate Student

Lead Presenter's Name

Danayit Mekonnen

Faculty Mentor Name

Dr.Kshitija Deshpande

Abstract

Ionospheric scintillation are signal perturbations caused by the interaction between the Earth’s geomagnetic field and the Sun’s activity and are apparent through rapid modifications in radio waves. Such perturbations are the most prevalent source of uncertainties in the position solution for Global Navigation Satellite Systems (GNSS). Since GNSS provide essential services for multiple industries and even everyday life, understanding ionospheric scintillation is essential. Geomagnetic storms are known to create disturbances in the ionosphere by increasing the total electron content (TEC). Therefore, this project highlights the relationship between geomagnetic storms and ionospheric scintillation through the analysis of processed GNSS data and proposes techniques for the identification and classification of scintillation in the mid-latitude region. Utilizing phase and amplitude data collected from two GPS Receivers installed in Daytona Beach, FL, possible events that correlate with scintillation observed during the storm were studied. A GI minor geomagnetic storm, measured to be -10 nanoTesla, that took place on January 31st, 2019 was studied because a significant spike in both phase and amplitude was observed. The implementation of machine learning is also explored through the development of an unsupervised k-means clustering algorithm that will identify the distribution of data points and classify scintillation.

Did this research project receive funding support (Spark or Ignite Grants) from the Office of Undergraduate Research?

No

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Investigation into the G1 Geomagnetic Storm of January 31st, 2019 through GNSS data processing.

Ionospheric scintillation are signal perturbations caused by the interaction between the Earth’s geomagnetic field and the Sun’s activity and are apparent through rapid modifications in radio waves. Such perturbations are the most prevalent source of uncertainties in the position solution for Global Navigation Satellite Systems (GNSS). Since GNSS provide essential services for multiple industries and even everyday life, understanding ionospheric scintillation is essential. Geomagnetic storms are known to create disturbances in the ionosphere by increasing the total electron content (TEC). Therefore, this project highlights the relationship between geomagnetic storms and ionospheric scintillation through the analysis of processed GNSS data and proposes techniques for the identification and classification of scintillation in the mid-latitude region. Utilizing phase and amplitude data collected from two GPS Receivers installed in Daytona Beach, FL, possible events that correlate with scintillation observed during the storm were studied. A GI minor geomagnetic storm, measured to be -10 nanoTesla, that took place on January 31st, 2019 was studied because a significant spike in both phase and amplitude was observed. The implementation of machine learning is also explored through the development of an unsupervised k-means clustering algorithm that will identify the distribution of data points and classify scintillation.

 

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