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Open AccessArticle
Detecting GPS Interference Using Automatic Dependent Surveillance-Broadcast Data
by
Akshay Ram Ramchandra
Akshay Ram Ramchandra
Anton Skurdal
Anton Skurdal
Prakash Ranganathan
Prakash Ranganathan
Dr. Prakash Ranganathan currently serves as an Associate Professor of Electrical Engineering and the [...]
Dr. Prakash Ranganathan currently serves as an Associate Professor of Electrical Engineering and the Director of the Data Energy Cyber and Systems (DECS) Laboratory at the University of North Dakota (UND). He earned his Ph.D. in Software Engineering and Electrical Engineering from North Dakota State University (NDSU). He is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). Driven by his passion for cutting-edge research, Dr. Ranganathan focuses his efforts on addressing complex challenges in areas such as smart grids, cyber security, data science, and uncertainty quantification in renewable energy. In addition to his role as the DECS Laboratory Director, Dr. Ranganathan serves as the Director for the Center for Cyber Security Research (C2SR) at UND, demonstrating his commitment to advancing the field of cybersecurity. His dedication and contributions have earned him numerous accolades, including the 2024 Outstanding Advisor Leadership Award, the 2019 Founders Day Award for Creative Work, the College of Engineering and Mines (CEM) Dean’s Outstanding Faculty Award in 2018, and the North Dakota Spirit Faculty Achievement Award in 2013, bestowed upon him by the UND Alumni Foundation. Notably, Dr. Ranganathan assumes a leadership position in cyber educational and research initiatives for the North Dakota University System (NDUS), reinforcing his dedication to advancing cybersecurity education.
William Semke
William Semke
Dr. William Semke currently serves as the Associate Dean for Academic Affairs for the College of and [...]
Dr. William Semke currently serves as the Associate Dean for Academic Affairs for the College of Engineering and Mines where he leads in the academic affairs at the undergraduate and graduate level, including college governance, and provides mentorship and guidance to students, faculty, and staff. He received a B.S. degree in Physics from Bemidji State University in May 1991, an M.S. degree in Engineering Mechanics and Astronautics from the University of Wisconsin-Madison in May 1993, and a Ph.D. degree in Mechanical Engineering from the University of Wisconsin-Madison in May 1999. He is also a Professor of Mechanical Engineering at the University of North Dakota, where he conducts contemporary research in UAS, vibration control, and aerospace hardware design, along with instruction in mechanical design and experimental methods.
1
School of Electrical Engineering & Computer Science, University of North Dakota, Grand Forks, ND 58202, USA
2
Department of Mechanical Engineering, University of North Dakota, Grand Forks, ND 58202, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2024, 13(16), 3145; https://doi.org/10.3390/electronics13163145 (registering DOI)
Submission received: 25 June 2024
/
Revised: 25 July 2024
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Accepted: 5 August 2024
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Published: 8 August 2024
Abstract
This paper investigates the detection of Global Positioning System (GPS) interference during the Dallas Fort Worth (DFW) event from 17 to 19 October 2022, utilizing various machine learning (ML) models. The study examines the effectiveness of several ML models, including neural networks (NN), tree-based models, regression-based models, Bayesian classifiers, distance-based models, and stochastic classifiers, in identifying GPS interference. A simulated training signature was created with 180,000 data points, of which 25,792 were modeled as positive samples indicating GPS interference. Preliminary results reveal that the Multi-Layer Perceptron (MLP) model outperformed others, achieving a 99.8% True Positive Rate (TPR). Additionally, permutation feature importance was utilized to understand how model feature prioritization impacts the detection outcomes. Given the increasing frequency of GPS interference, these findings underscore the critical importance of ML techniques in detecting GPS interference patterns in Automatic Dependent Surveillance-Broadcast (ADS-B) data.
Share and Cite
MDPI and ACS Style
Ramchandra, A.R.; Skurdal, A.; Ranganathan, P.; Semke, W.
Detecting GPS Interference Using Automatic Dependent Surveillance-Broadcast Data. Electronics 2024, 13, 3145.
https://doi.org/10.3390/electronics13163145
AMA Style
Ramchandra AR, Skurdal A, Ranganathan P, Semke W.
Detecting GPS Interference Using Automatic Dependent Surveillance-Broadcast Data. Electronics. 2024; 13(16):3145.
https://doi.org/10.3390/electronics13163145
Chicago/Turabian Style
Ramchandra, Akshay Ram, Anton Skurdal, Prakash Ranganathan, and William Semke.
2024. "Detecting GPS Interference Using Automatic Dependent Surveillance-Broadcast Data" Electronics 13, no. 16: 3145.
https://doi.org/10.3390/electronics13163145
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