Machine Learning-assisted GNSS Interference Monitoring through Crowdsourcing

Raichur, Nisha Lakshmana; Brieger, Tobias; Jdidi, Dorsaf; Feigl, Tobias; Merwe, J. Rossouw van der; Ghimire, Birendra; Ott, Felix; Rügamer, Alexander; Felber, Wolfgang

doi:10.33012/2022.18492

Cited by 3 publications

(6 citation statements)

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“…Evaluating the CN0 is a simple post-processing method and can be applied independently of the GNSS receiver hardware. Therefore, it is a popular method to detect interference signals with Android smartphones [ 40 , 41 , 42 , 43 ], automatic dependent surveillance-broadcast (ADS-B) flight data [ 44 ], or publicly available data [ 45 ].…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

“…A significant issue with this approach is the detector tuning and calibration in practical systems. An implicit energy detection method is monitoring the automatic gain control (AGC) of a GNSS receiver, as it regulates the front end gain based on the received signal energy [ 43 , 49 ]. However, it is risky that the AGC is considered a black box, and unknown effects could deteriorate detection performance.…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

“…Traditional classification has several limitations: the effort of deriving and tuning optimal detectors, the inability to adapt to new waveforms without new development, high processing costs, and complex logic trees. The latest state-of-the-art techniques employ ML or deep learning (DL) methods to compensate for these weaknesses and to reliably and accurately classify interference signals [ 36 , 43 , 57 , 58 ]. Modern ML- and DL-based methods are on par with or even superior to classical techniques in practical situations, as these methods learn non-deterministic and non-linear correlations from data.…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

“…Once an interference signal is detected, a permanent solution to stop it is to find and remove it. Therefore, locating interference sources is necessary, and many solutions exist [ 15 , 43 , 49 , 69 , 70 , 71 , 72 ]. A popular approach is to collaborate between receivers.…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

“…Compared with the original implementation [ 36 ], the ML pipeline is slightly tuned through experience with realistic experiments [ 43 , 57 , 58 , 94 ]. It is now more adequate for handling different data input types, i.e., time series such as C/N0, AGC, and elevation and images of spectrograms.…”

Section: Algorithmic Designmentioning

confidence: 99%

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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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