Intentional GNSS Interference Detection and Characterization Algorithm Using AGC and Adaptive IIR Notch Filter

Yang, Jing; Kang, Chang Ho; Kim, Sun Young; Park, Chan Gook

doi:10.5139/ijass.2012.13.4.491

Cited by 17 publications

(6 citation statements)

References 10 publications

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“…It improves the classification performance and robustness even in multipath and dynamic cases. In contrast to classical model-driven classification techniques, supervised data-driven approaches implicitly learn these thresholds or informative patterns, enabling accurate classification even near the noise floor [ 59 ]. In addition, methods based on artificial intelligence (AI) use exogenous information such as multipath components or other interference sources to map even more specific patterns and specific classes [ 60 ].…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Merwe

Franco

Hansen

et al. 2023

Sensors

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Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

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Section: Background To Interference Monitoringmentioning

confidence: 99%

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Merwe

Franco

Hansen

et al. 2023

Sensors

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“…Also in this case, g r ½n is compared against a decision threshold. Several other metrics can be derived from the AGC count which can be coupled with other approaches for reveling the presence of jamming [32], [38].…”

Section: A Hardware Indicatorsmentioning

confidence: 99%

Impact and Detection of GNSS Jammers on Consumer Grade Satellite Navigation Receivers

et al. 2016

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| Jamming is the act of intentionally directing powerful electromagnetic waves toward a victim receiver with the ultimate goal of denying its operations. This paper describes the main types of Global Navigation Satellite System (GNSS) jammers and reviews their impact on GNSS receivers. A survey of state-of-the-art methods for jamming detection is also provided. Different detection approaches are investigated with respect to the receiver stage where they can be implemented.

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“…Thus, the SNR measurement is set as 23 dB in this study. The MATLAB‐based GPS simulator [23] and receiver [24] are used to generate the GPS L1 signal. The simulator generates an IF signal data of the GPS L1 signal based on a mathematical model and the IF is 9.548 MHz and the sampling frequency is 38.192 MHz.…”

Section: Simulationsmentioning

confidence: 99%

Adaptive complex‐EKF‐based DOA estimation for GPS spoofing detection

Kang

Kim

Park

2018

IET signal process.

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In this study, a simple but effective spoofing detection method using a global positioning system (GPS) directional antenna is proposed which exploits the difference between the estimated direction-of-arrival (DOA) and the measured DOA from a GPS almanac and ephemeris data. The receiving signal's DOA is estimated by using a single antenna power measurementbased complex extended Kalman filter (EKF) which is a complex valued state space based estimation technique. Furthermore, an adaptive logic is applied to the complex EKF to reduce the effect of the measurement disturbance. To maintain the validity of the proposed algorithm, it is assumed that the spoofer is aware of the target's location, but that its DOA is not perfectly the same as that of the authentic GPS signal. In addition, the orientation of the directional antenna is known by using an attitude and heading reference system that is attached to the antenna, and its antenna radiation pattern is also known. The proposed detection method is evaluated using a theoretical analysis and simulations. It is finally confirmed that the proposed algorithm can detect a spoofing signal according to the different direction angles of the spoofing signal, and especially those with low DOAs.

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Intentional GNSS Interference Detection and Characterization Algorithm Using AGC and Adaptive IIR Notch Filter

Cited by 17 publications

References 10 publications

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System

Impact and Detection of GNSS Jammers on Consumer Grade Satellite Navigation Receivers

Adaptive complex‐EKF‐based DOA estimation for GPS spoofing detection

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