Datasets
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GNSS Spectrum Highway Dataset 1
- Citation Author(s):
-
FelixOtt
Fraunhofer Institute for Integrated Circuits IISLucasHeubleinFraunhofer Institute for Integrated Circuits IISTobiasFeiglFraunhofer Institute for Integrated Circuits IISAlexanderRügamerFraunhofer Institute for Integrated Circuits IIS - Submitted by:
- Felix Ott
- Last updated:
- Sat, 11/02/2024 - 14:12
- DOI:
- 10.21227/xpm9-tt28
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- Research Article Link:
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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. We recorded a dataset with our own sensor station at a German highway with eight interference classes and three non-interference classes. Our baseline methods achieve an accuracy of 97.66%. This dataset allows the development and evaluation of machine learning methods, such as domain adaptation, few-shot learning, and continual learning.
Introduction
We publish the Spectrum Highway Dataset 1 that was recorded on a bridge over a German highway and contains a variety of interference classes in GNSS signals from spectral recording hardware. For the dataset recording, we developed a hardware setup that captures short, wideband snapshots in both E1 and E6 GNSS bands. This setup is mounted to a bridge over a highway. The setup records 20ms raw IQ snapshots triggered from the energy with a sample rate of 62.5MHz, an analog bandwidth of 50MHz and an 8bit bit-width.
The dataset can be used for research purposes to addresse the following research problems:
- GNSS interference detection
- GNSS interference classification with 8 interference classes
- Adaptation to new interference classes by addresses the following ML topics:
- Domain adaptation
- Few-shot learning
- Continual learning
- Interference mitigation
- ML architecture search
- Addressing robustness of ML models in unbalanced datasets
Dataset
The folder GNSS_dataset contains the Spectrum Highway Dataset 1 with the corresponding labels in .txt files for the train-(adaptation-)test split. To use the dataset extract all .npy files from the .zip files. One sample in the text file is defined as for example:
data/157369.npy 0
For classical ML methods, use the train.txt and test.txt dataset to train and evaluate your model. For methods such as few-shot learning, use the train.txt dataset for the baseline training, the FSL_support_set.txt as the support datset, the FSL_adaptation_set.txt as the adaptation dataset, and FSL_adaptation_test.set for the final testing dataset. The test.set is split into the FSL_adaptation_set.txt and FSL_adaptation_test.set datasets.
At certain frequencies the GPS/Galileo or GLONASS signals can easily be seen as a slight increase in the spectrum. Note that experts manually analyzed the datastreams by thresholding CN/0 and AGC values. Manual labeling of these snapshots has resulted in 11 classes: Classes 0 to 2 represent samples with no interferences, distinguished by variations in background intensity, while classes 3 to 10 contain different interferences.
License
This work is licensed under a CC BY-NC-SA 4.0: Creative Commons Attribution-Noncommercial-ShareAlike, see https://creativecommons.org/licenses/by-nc-sa/4.0/
Dataset Files
- Subset 00 00.tar.gz (13.01 GB)
- Subset 01 01.tar.gz (13.03 GB)
- Subset 02 02.tar.gz (13.03 GB)
- Subset 03 03.tar.gz (13.03 GB)
- Subset 04 04.tar.gz (13.03 GB)
- Subset 05 05.tar.gz (13.03 GB)
- Readme file and labels files stored in .txt format readme_label.zip (575.43 kB)
