Multimodal Learning for Reliable Interference Classification in GNSS Signals

Brieger T, Raichur NL, Jdidi D, Ott F, Feigl T, Van Der Merwe JR, Ruegamer A, Felber W (2022)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Pages Range: 3210-3234

Conference Proceedings Title: Proc. Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+)

Event location: Denver, CO US

DOI: 10.33012/2022.18586


Interference signals affect the processing chain of the Global Navigation Satellite System (GNSS) and so, degrade its localization accuracy. Therefore, potential interference signals must be mitigated or a potential transmitter (i.e., jammer) eliminated. However, to successfully remove the interference signals, they must first be detected and then localized. In addition, the successful classification of the waveform of an interference signal helps to deduce the signal’s purpose, thus simplifying its localization. Recently, snapshot-based data-driven methods, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) [10], outperformed classic model-based techniques, such as pattern recognition and mathematical formulations, as they enable high classification accuracy even under challenging scenarios. One of the reasons for this is that they can learn a mapping approximation directly from data that implicitly describe non-deterministic or nonlinear functions without any additional modeling effort. However, these classical learning-based methods either do not use time and context at all, e.g., Random Forest [2], SVM [11], and CNN [4], only consider time-dependent local phenomena (i.e., spatial features) in snapshots, e.g., ResNet [5] and Temporal CNN (TCN) [14], or only consider time-dependent global phenomena (i.e., time-sensitive features) in sequences but ignore local phenomena, e.g., Recurrent Neural Networks (RNN). To improve the mitigation of interference signals and incorporate both spatial and time-sensitive features, we propose a novel Multimodal Learning (MML) system that improves the classification accuracy beyond the state-of-the-art, considers the uncertainty of its estimates and lowers the computation and energy costs significantly. To this end, our MML approach complements the most prominent methods from the literature that claims to provide the most robust and accurate classification to enable multimodal embedding of inputs. Our experiments show that our MML framework with late fusion mechanics learns to implicitly weigh between spatial (images of spectrum data) and time-sensitive (matrix of raw IQ samples) features, as it provides reliable interference classification. We use realistic, deterministic data from our large-scale real measurement campaign covering five sources of interference signals and multipath effects to evaluate state-of-the-art methods and our MML framework. To the best of our knowledge, we are the first to investigate data-driven multimodal fusion methods on real-world data to create energy-efficient multipath-resistant classification algorithms that may adapt to various types of input and artifacts thereof.

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Brieger, T., Raichur, N.L., Jdidi, D., Ott, F., Feigl, T., Van Der Merwe, J.R.,... Felber, W. (2022). Multimodal Learning for Reliable Interference Classification in GNSS Signals. In Proc. Intl. Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+) (pp. 3210-3234). Denver, CO, US.


Brieger, Tobias, et al. "Multimodal Learning for Reliable Interference Classification in GNSS Signals." Proceedings of the Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+), Denver, CO 2022. 3210-3234.

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