Unsupervised Disentanglement for PostIdentification of GNSS Interference in the Wild

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

doi:10.33012/2022.18493

Cited by 3 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML is a popular modern choice for signal classification and shows good performance in various applications [2], [18], [19]. It especially shows improved resilience to classification in scenarios where the interference signals are affected by multipath [20], [21]. However, it has several limitations.…”

Section: Interference Signals Degrade Gnss Servicesmentioning

confidence: 99%

“…The ML training is more stable and has less training overhead compared to the left extreme, but it also has more opportunities than the right extreme. The current state-of-the-art design uses approaches like CNNs [18]- [20] and twin neural networks (TNNs) [21]. However, the pre-processing still results in large dimensions, i.e., the STFT is still significant.…”

Section: Interference Signals Degrade Gnss Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Machine Learning and Signal Processing Synergies for Low-Resource GNSS Interference Classification

van der Merwe,

Franco,

Feigl

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

Interference signals degrade the performance of a global navigation satellite system (GNSS) receiver. Classification of these interference signals allow better situational awareness and facilitate appropriate counter-measures. However, classification is challenging and processing-intensive, especially in severe multipath environments. This article proposes a low-resource interference classification approach that combines conventional statistical signal processing approaches with machine learning (ML). It leverages the processing efficiency of conventional statistical signal processing by summarizing, e.g., a short-time Fourier transform (STFT), with statistical measures. Furthermore, the ML design space is bounded as the signal is pre-processed. It results in fewer opportunities for ML but facilitates faster convergence and the use of simpler architectures. Therefore, this approach has lower ML training complexity and lower processing and memory requirements. Results show competitive classification capabilities to more complex approaches. It demonstrates that more efficient architectures can be developed using existing signal-processing approaches.

show abstract

Section: Interference Signals Degrade Gnss Servicesmentioning

confidence: 99%

Section: Interference Signals Degrade Gnss Servicesmentioning

confidence: 99%

Optimal Machine Learning and Signal Processing Synergies for Low-Resource GNSS Interference Classification

van der Merwe,

Franco,

Feigl

et al. 2024

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

“…Statistical methods incorporating a more diverse range of pre-processed features may benefit using SVMs [ 66 , 67 ]. Recently, autoencoders (AEs) have also proven useful [ 58 , 68 ]. ML has a significant advantage in learning critical information and automatically optimizing the classification process for optimal performance.…”

Section: Background To Interference Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

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

Few-Shot Learning with Uncertainty-Based Quadruplet Selection for Interference Classification in GNSS Data

Ott,

Heublein,

Raichur

et al. 2024

2024 International Conference on Localization and GNSS (ICL-GNSS)

View full text Add to dashboard Cite

Unsupervised Disentanglement for PostIdentification of GNSS Interference in the Wild

Cited by 3 publications

References 0 publications

Optimal Machine Learning and Signal Processing Synergies for Low-Resource GNSS Interference Classification

Optimal Machine Learning and Signal Processing Synergies for Low-Resource GNSS Interference Classification

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

Few-Shot Learning with Uncertainty-Based Quadruplet Selection for Interference Classification in GNSS Data

Contact Info

Product

Resources

About