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

• URL: http://arxiv.org/abs/2402.09466v2
• Date: Thu, 2 May 2024 07:17:50 GMT
• Title: Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data
• Authors: Felix Ott, Lucas Heublein, Nisha Lakshmana Raichur, Tobias Feigl, Jonathan Hansen, Alexander Rügamer, Christopher Mutschler,
• Abstract summary: 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.
• Score: 40.40418209489273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. In this paper, 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. Furthermore, our quadruplet variant selects pairs based on the aleatoric and epistemic uncertainty to differentiate between similar classes. We recorded a dataset at a motorway with eight interference classes on which our FSL method with quadruplet loss outperforms other FSL techniques in jammer classification accuracy with 97.66%. Dataset available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway

Related papers

• Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization [42.14439854721613]
  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.
  arXiv  Detail & Related papers  (2024-09-23T15:20:33Z)
• 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)
• Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift [12.443489826220183]
  Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for automated wireless device identification and classification. It faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem.
  arXiv  Detail & Related papers  (2024-03-06T20:33:55Z)
• DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction [10.775623936099173]
  Previous semi-supervised learning methods typically match model predictions of different data-augmented views in a single-level interaction manner. We propose a novel SSL method called DualMatch, in which the class prediction jointly invokes feature embedding in a dual-level interaction manner. In the standard SSL setting, the proposal achieves 9% error reduction compared with SOTA methods, even in a more challenging class-imbalanced setting, the proposal can still achieve 6% error reduction.
  arXiv  Detail & Related papers  (2023-10-25T08:34:05Z)
• OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers [71.08167292329028]
  We propose a novel Open-set Semi-Supervised Learning (OSSL) approach called OpenMatch. OpenMatch unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers. It achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.
  arXiv  Detail & Related papers  (2021-05-28T23:57:15Z)
• Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference [150.07326223077405]
  Few-shot learning is attracting much attention to mitigate data scarcity. We present a discriminative nearest neighbor classification with deep self-attention. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model.
  arXiv  Detail & Related papers  (2020-10-25T00:39:32Z)
• Deep F-measure Maximization for End-to-End Speech Understanding [52.36496114728355]
  We propose a differentiable approximation to the F-measure and train the network with this objective using standard backpropagation. We perform experiments on two standard fairness datasets, Adult, Communities and Crime, and also on speech-to-intent detection on the ATIS dataset and speech-to-image concept classification on the Speech-COCO dataset. In all four of these tasks, F-measure results in improved micro-F1 scores, with absolute improvements of up to 8% absolute, as compared to models trained with the cross-entropy loss function.
  arXiv  Detail & Related papers  (2020-08-08T03:02:27Z)
• Multi-scale Interactive Network for Salient Object Detection [91.43066633305662]
  We propose the aggregate interaction modules to integrate the features from adjacent levels. To obtain more efficient multi-scale features, the self-interaction modules are embedded in each decoder unit. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably against 23 state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-07-17T15:41:37Z)

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.