Announcements of Opportunity
SURF: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty and JPL technical staff for the SURF program.
Each AO indicates whether or not it is open to non-Caltech students. If an AO is NOT open to non-Caltech students, please DO NOT contact the mentor.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor. Click here for more tips on finding a mentor.
Announcements for external summer programs are listed here.
New for 2021: Students applying for JPL projects should complete a SURF@JPL application instead of a "regular" SURF application.
Students pursuing opportunities at JPL must be
U.S. citizens or U.S. permanent residents.
|Project:||Machine Learning Based Prediction of Ionospheric Irregularities (ML-PRIIS)|
|Disciplines:||Electrical Engineering and Computer Science, Computer Science|
Irregularly distributed electrons in the Earths upper atmosphere, known as ionospheric irregularities, can cause scintillation in power and phase of radio signals that traverse the ionosphere. This results in degradation and interruption of signals of global navigation satellite system (GNSS), its augmentation systems, radio communications, and satellite-based remote sensing radar systems.
The Wide Area Augmentation System (WAAS) developed under the administration of the U.S. Federal Aviation Administration (FAA) is such a system that supports aviation navigation using GNSS and mitigates ionospheric effects. One of the research projects conducted at JPL is to predict ionospheric irregularities, including their occurrence time and location as well as intensity under various space weather and geophysical conditions.
Research will be conducted for the PRIIS model development using GNSS data and machine-learning (ML) techniques. The student will participate in processing of GNSS tracking data, characterization of ionospheric irregularities using the GNSS data, and application of ML techniques. Through the research, the student will learn satellite navigation, GNSS (including GPS, GLONASS, Beidou, and Galileo systems), its tracking data, WAAS, ionosphere, space weather, geomagnetic storms, and ML techniques. These ML techniques will also be applicable to a variety of other data-driven predictions.
Acronyms (in the order of appearance)
PRIIS Prediction of Ionospheric Irregularities
ML Machine Learning
GNSS Global Navigation Satellite System
GPS Global Positioning System
GLONASS Global Navigation Satellite System (Russians system)
|Student Requirements:||Knowledge of machine learning algorithms and application experience are suggested. Python programming skills are required.|
|Location / Safety:||Project building and/or room locations: . Student will need special safety training: .|
This AO can be done under the following programs:
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