Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)                   IJEEE 2024, 20(4): 3348-3348 | Back to browse issues page

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Jahantab M J, Tohidi S, Mosavi M R, Ayatollahi A. GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model. IJEEE 2024; 20 (4) :3348-3348
URL: http://ijeee.iust.ac.ir/article-1-3348-en.html
GPS Spoofing Detection using CAF Images and Neural Networks Based on the Proposed Peak Mapping Dimensionality Reduction Algorithm and TCNN Model
M. J. Jahantab , S. Tohidi , Mohammad Reza Mosavi , Ahmad Ayatollahi
Abstract:   (294 Views)
Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.
Keywords: GPS, Spoofing Detection, CAF, TCNN, Dimension Reduction Algorithm
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Type of Study: Research Paper | Subject: Signal Processing
Received: 2024/07/04 | Revised: 2024/12/02 | Accepted: 2024/11/03
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.