Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization

• URL: http://arxiv.org/abs/2409.15114v1
• Date: Mon, 23 Sep 2024 15:20:33 GMT
• Title: Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization
• Authors: Lucas Heublein, Tobias Feigl, Thorsten Nowak, Alexander Rügamer, Christopher Mutschler, Felix Ott,
• Abstract summary: Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS) The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. This paper introduces an extensive dataset capturing interferences within a large-scale environment including controlled multipath effects.
• Score: 42.14439854721613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices. This paper introduces an extensive dataset compromising snapshots obtained from a low-frequency antenna, capturing diverse generated interferences within a large-scale environment including controlled multipath effects. Our objective is to assess the resilience of ML models against environmental changes, such as multipath effects, variations in interference attributes, such as the interference class, bandwidth, and signal-to-noise ratio, the accuracy jamming device localization, and the constraints imposed by snapshot input lengths. By analyzing the aleatoric and epistemic uncertainties, we demonstrate the adaptness of our model in generalizing across diverse facets, thus establishing its suitability for real-world applications. https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency

Related papers

• Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies [36.738256927742526]
  Vehicle localization is crucial for applications such as self-driving cars, toll systems, and digital tachographs. To achieve accurate positioning, vehicles typically use global navigation satellite system (GNSS) receivers to validate their absolute positions. Recent approaches based on machine learning (ML) have shown superior performance in monitoring interference. We evaluate the latest supervised ML-based methods to report on their performance in real-world settings.
  arXiv  Detail & Related papers  (2025-03-31T06:51:52Z)
• Multimodal-to-Text Prompt Engineering in Large Language Models Using Feature Embeddings for GNSS Interference Characterization [2.469551405169408]
  Large language models (LLMs) are advanced AI systems applied across various domains, including NLP, information retrieval, and recommendation systems. interference monitoring is essential to ensure the reliability of vehicle localization on roads. Our pipeline outperforms state-of-the-art machine learning models in interference classification tasks.
  arXiv  Detail & Related papers  (2025-01-09T09:01:04Z)
• Adaptive Signal Analysis for Automated Subsurface Defect Detection Using Impact Echo in Concrete Slabs [0.0]
  This pilot study presents a novel, automated, and scalable methodology for detecting subsurface defect-prone regions in concrete slabs. The approach integrates advanced signal processing, clustering, and visual analytics to identify subsurface anomalies. The results demonstrate the robustness of the methodology, consistently identifying defect-prone areas with minimal false positives and few missed defects.
  arXiv  Detail & Related papers  (2024-12-23T20:05:53Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling [44.24482830284491]
  jamming devices compromise accuracy of global navigation satellite system (GNSS) receivers. We propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios.
  arXiv  Detail & Related papers  (2024-10-03T11:07:17Z)
• Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation [91.83820250747935]
  Pseudo-label noise is mainly contained in unstable samples in which predictions of most pixels undergo significant variations during self-training. We introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples. SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings.
  arXiv  Detail & Related papers  (2024-06-10T21:44:52Z)
• Denoising of Geodetic Time Series Using Spatiotemporal Graph Neural Networks: Application to Slow Slip Event Extraction [2.659901499016884]
  We design SSE SSEdenois, a graph-based attentive denoiser that learns latent characteristics of noise to reveal SSE-related displacement with sub-millimeter precision. The proposed method is applied to the Cascadia subduction zone, where SSEs occur along with rumbling of tectonic tremors.
  arXiv  Detail & Related papers  (2024-05-06T09:55:11Z)
• Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data [40.40418209489273]
  Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS) We propose a few-shot learning (FSL) approach to adapt to new interference classes. Our method employs quadruplet selection for the model to learn representations using various positive and negative interference classes.
  arXiv  Detail & Related papers  (2024-02-09T13:59:14Z)
• DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
  Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. We introduce DARTH, a holistic test-time adaptation framework for MOT.
  arXiv  Detail & Related papers  (2023-10-03T10:10:42Z)
• Novel features for the detection of bearing faults in railway vehicles [88.89591720652352]
  We introduce Mel-Frequency Cepstral Coefficients (MFCCs) and features extracted from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults.
  arXiv  Detail & Related papers  (2023-04-14T10:09:50Z)
• Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning [86.08285033925597]
  This paper presents a new DP perturbation mechanism with a time-varying noise amplitude to protect the privacy of federated learning. We derive an online refinement of the series to prevent FL from premature convergence resulting from excessive perturbation noise. The contribution of the new DP mechanism to the convergence and accuracy of privacy-preserving FL is corroborated, compared to the state-of-the-art Gaussian noise mechanism with a persistent noise amplitude.
  arXiv  Detail & Related papers  (2023-03-07T22:52:40Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• Deep Interference Mitigation and Denoising of Real-World FMCW Radar Signals [16.748215232763517]
  We evaluate a Convolutional Neural Network (CNN)-based approach for interference mitigation on real-world radar measurements. We combine real measurements with simulated interference in order to create input-output data suitable for training the model.
  arXiv  Detail & Related papers  (2020-12-04T11:22:13Z)
• Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring [0.0]
  This paper uses an uncertainty-aware deep neural distribution network framework to learn robust localization models. We show that the predictive uncertainty scales as environmental uncertainty increases to provide a statistically meaningful metric for assessing localization accuracy.
  arXiv  Detail & Related papers  (2020-07-14T04:53:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.