Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916827933704192 |
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| author | Heublein, Lucas Feigl, Tobias Rügamer, Alexander Ott, Felix |
| author_facet | Heublein, Lucas Feigl, Tobias Rügamer, Alexander Ott, Felix |
| contents | The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust classification of interference using machine learning (ML) models is challenging due to the lack of labeled data in real-world environments. In this paper, we propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. We present a semi-supervised approach coupled with an uncertainty-based voting mechanism by combining Monte Carlo and Deep Ensembles that effectively minimizes the requirement for labeled training samples to less than 5% of the dataset while improving adaptability across varying environments. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14686 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling Heublein, Lucas Feigl, Tobias Rügamer, Alexander Ott, Felix Signal Processing Artificial Intelligence Machine Learning 94-05, 82-11 E.0; I.2.0; I.5.4; I.5.1 The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust classification of interference using machine learning (ML) models is challenging due to the lack of labeled data in real-world environments. In this paper, we propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. We present a semi-supervised approach coupled with an uncertainty-based voting mechanism by combining Monte Carlo and Deep Ensembles that effectively minimizes the requirement for labeled training samples to less than 5% of the dataset while improving adaptability across varying environments. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios. |
| title | Achieving Generalization in Orchestrating GNSS Interference Monitoring Stations Through Pseudo-Labeling |
| topic | Signal Processing Artificial Intelligence Machine Learning 94-05, 82-11 E.0; I.2.0; I.5.4; I.5.1 |
| url | https://arxiv.org/abs/2410.14686 |