GNSS cloud‐data processing technique for jamming detection, identification, and localisation

Kim, Hyeong-Pil; Jin, Gwon-Gyu; Won, Jong-Hoon

doi:10.1049/iet-rsn.2019.0518

Cited by 3 publications

(1 citation statement)

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“…They prove the detection of jammer type and estimate localisation based on the 2 dimensional timefrequency correlation between receivers. Kim et al (Kim, Jin, and Won 2020) again show the validity of considering the jamming signal as a 2 dimensional correlation in the time-frequency domain for detection of jamming type and estimated localisation. As in (Lee, Kim, and Won 2018) this solution is based on generating a network of receivers.…”

Section: Figure 1graphical Representation Of Jamming Signal Typesmentioning

confidence: 64%

GPS Jamming Signal Classification with CNN Feature Extraction in low Signal-to-Noise Environments

Swinney¹,

Woods²

2022

IJCSA

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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 to cause major disruption to many services including financial, power distribution and communication systems. Recent testing assesses Global Navigation Satellite System (GNSS) jammers as being very dangerous to aircraft and Unmanned Aerial Vehicles (UAVs) especially those flying at low height. GNSS is also critical to the safe operation of Connected and Autonomous Vehicles (CAV) such as driverless cars. Timely detection of an attack is deemed to be enough to ensure the safety of the vehicle. Detection and classification of GNSS jamming signals is necessary to enable this. This paper considers feature extraction using a Convolutional Neural Network (CNN) when representing the signal as a graphical image. The JamDetect dataset is produced containing 6 different types of commercial jamming signals. Features are extracted using a CNN before a machine learning classifier is trained for classification. Results show that representing the signal in the graphical form of Power Spectral Density (PSD) is the least susceptible to noise. CNN feature extraction with machine learning classifier Logistic Regression using PSD produces 82.7% (+/-0.7%) at -20dB SNR and 100% accuracy at -10dB SNR. The results using PSD graphical signal representation are significant for when it is necessary to detect and classify GPS jamming signals in low SNR environments.

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Section: Figure 1graphical Representation Of Jamming Signal Typesmentioning

confidence: 64%