GPS Jamming Signal Classification with CNN Feature Extraction in low Signal-to-Noise Environments
نویسندگان
چکیده
The Global Positioning System (GPS) is a satellite constellation which gives users access to position, navigation and timing services. Many industries not only benefit from this but are reliant on it. Although illegal, GPS jamming devices have the power cause major disruption many services including financial, distribution communication systems. Recent testing assesses Navigation Satellite (GNSS) jammers as being very dangerous aircraft Unmanned Aerial Vehicles (UAVs) especially those flying at low height. GNSS also critical safe operation of Connected Autonomous (CAV) such driverless cars. Timely detection an attack deemed be enough ensure safety vehicle. Detection classification signals necessary enable this. This paper considers feature extraction using Convolutional Neural Network (CNN) when representing signal graphical image. JamDetect dataset produced containing 6 different types commercial signals. Features extracted CNN before machine learning classifier trained for classification. Results show that in form Power Spectral Density (PSD) least susceptible noise. with Logistic Regression PSD produces 82.7% (+/-0.7%) -20dB SNR 100% accuracy -10dB SNR. results representation significant it detect classify environments.
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ژورنال
عنوان ژورنال: International journal on cyber situational awareness
سال: 2022
ISSN: ['2057-2182', '2633-495X']
DOI: https://doi.org/10.22619/ijcsa.2021.100135