Low-cost COTS GNSS interference detection and classification platform: Initial results

Merwe, J. Rossouw van der; Franco, David Contreras; Jdidi, Dorsaf; Feigl, Tobias; Rügamer, Alexander; Felber, Wolfgang

doi:10.1109/icl-gnss54081.2022.9797025

Cited by 3 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This article extends the conference paper [ 36 ] in four ways. First, it presents an extended hardware description of the system, including an analysis of the power consumption, methods to reduce the power consumption, and an investigation of alternative hardware designs.…”

Section: Introductionmentioning

confidence: 58%

“…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%

“…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%

“…Figure 10 depicts the processing pipeline. The supervised pipeline was first introduced by van der Merwe et al [ 36 ] and Brieger et al [ 57 ]. Data flows from the left (input to the model) to the right (predictions of the model).…”

Section: Algorithmic Designmentioning

confidence: 99%

“…Third, the ML algorithms are explored more deeply to reveal the novel pipeline for interference detection and classification, which facilitates unsupervised label generation to detect and classify unknown interference signals reliably. Fourth, the results of [ 36 ] are extended by incorporating more trials to fortify the previous results. Most interesting is the inclusion of tests in a large anechoic chamber with a PPD mounted inside a car for realistic wave propagation effects.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Merwe

Franco

Hansen

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 58%