International Journal of Railway Research
Iran University of Science & Technology
Wed, Jul 9, 2025
[Archive]
Volume 12, Issue 1 (1-2025)
IJRARE 2025, 12(1): 71-80 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Sormayli J, Mosavi M R. Semi-Supervised GPS Interference Detection: Enhancing Safety in Electrified Railway Networks. IJRARE 2025; 12 (1) :71-80
URL: http://ijrare.iust.ac.ir/article-1-374-en.html
URL: http://ijrare.iust.ac.ir/article-1-374-en.html
School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: (68 Views)
The Global Positioning System (GPS) is integral to the safety and efficiency of modern railway networks; however, its susceptibility to jamming and spoofing interference poses a significant threat to operational integrity. Conventional detection systems often rely on fully supervised models requiring extensive labeled data or specialized, costly hardware, limiting their scalability. This paper addresses this gap by proposing and evaluating an Enhanced Semi-Supervised K-Means (ESS-KMeans) algorithm designed to operate effectively with minimal labeled data. We compare its performance against a standard unsupervised K-Means algorithm using a challenging, synthetically generated dataset based on GPS signal characteristics such as latitude/longitude variation, altitude deviation, and Automatic Gain Control (AGC) levels. The proposed ESS-KMeans leverages a small labeled subset for robust centroid initialization and mutual information-based feature weighting, while also uniquely identifying and flagging ambiguous, low-confidence samples. Experimental results demonstrate that ESS-KMeans achieves perfect (1.000) accuracy on confidently classified samples, a significant improvement over standard K-Means (0.960), and improves cluster quality by over 45% (Silhouette Score). By delivering superior accuracy and providing a mechanism for uncertainty quantification with minimal supervision, this semi-supervised approach presents a scalable, cost-effective, and reliable solution for enhancing the resilience of railway systems against GPS interference.
Type of Study: Research |
Subject:
Railway Transportation
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
