FelixOtt/FIOT_highway

Spectrum Highway Dataset 1 for Interference Classification in GNSS Data

Latest update: 2024-10-30

Project size: 342 GB

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 training set train.txt in combination with the adaptation set FSL_adaptation_test.txt, and the testing set test.txt to train and evaluate your model (see also the website https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/gnss_cross_validation). 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.txt for the final testing dataset. The test.set is split into the FSL_adaptation_set.txt and FSL_adaptation_test.set datasets.

The following figure shows exemplary snapshots of the spectrogram. 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.

The following table presents a summary of the sample distributions across all 11 classes. This shows the dataset's imbalance, emphasizing the under-representation of positive class labels.

Class	Label	Sample number
Negative (low intensity)	0	9,980
Negative (medium intensity)	1	132,974
Negative (hgih intensity)	2	54,620
Positive (pulsed)	3	13
Positive (pulsed)	4	28
Positive (out-of-band tone)	5	59
Positive (noise)	6	9
Positive (tone)	7	39
Positive (chirp)	8	79
Positive (two chirps)	9	10
Positive (chirp)	10	16

For our spectrum highway dataset 2 recorded along a different highway bridge, see the following website: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/fiot_highway2

For our dataset recorded in a controlled environment with a high-frequency antenna, see the following website: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_LC_laboratory

For our dataset recorded in a controlled environment with a low-frequency antenna, see the following website: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/controlled_low_frequency

References

For more information of the dataset and results, see our publication. If you use our dataset for your research, please consider citing:

@inproceedings{ott_heublein_icl,
  author = {Felix Ott and Lucas Heublein and Nisha Lakshmana Raichur and Tobias Feigl and Jonathan Hansen and Alexander Rügamer and Christopher Mutschler},
  title = {{Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data}},
  booktitle = {\href{https://ieeexplore.ieee.org/document/10578525}{IEEE Intl. Conf. on Localization and GNSS (ICL-GNSS)}},
  month = jun,
  year = {2024},
  address = {Antwerp, Belgium},
  doi = {10.1109/ICL-GNSS60721.2024.10578525}
}

For further related publications, see:

@inproceedings{raichur_heublein,
  author = {Nisha L. Raichur and Lucas Heublein and Tobias Feigl and Alexander Rügamer and Christopher Mutschler and Felix Ott},
  title = {{Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning}},
  booktitle = {\href{https://arxiv.org/html/2405.11067v1}{arXiv preprint arXiv:2405.11067v1}},
  month = jul,
  year = {2024}
}

@inproceedings{heublein_raichur_ion,
  author = {Lucas Heublein and Nisha L. Raichur and Tobias Feigl and Tobias Brieger and Fin Heuer and Lennart Asbach and Alexander Rügamer and Felix Ott},
  title = {{Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies}},
  booktitle = {\href{https://www.ion.org/publications/abstract.cfm?articleID=19887}{Proc. of the Intl. Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+)}},
  pages = {1260--1293},
  month = sep,
  year = 2024,
  address = {Baltimore, MD},
  doi = {10.33012/2024.19887}
}

Acknowledgment

This work has been carried out within the DARCII project, funding code 50NA2401, supported by the German Federal Ministry for Economic Affairs and Climate Action (BMWK), managed by the German Space Agency at DLR and assisted by the Bundesnetzagentur (BNetzA) and the Federal Agency for Cartography and Geodesy (BKG). Additionally, this work was supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy through the Center for Analytics – Data – Applications (ADA-Center) within the framework of „BAYERN DIGITAL II“ (20-3410-2-9-8).

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/

Kontakt

If you have any questions or tips to improve the datasets, contact us:

Felix Ott: felix.ott@iis.fraunhofer.de

Lucas Heublein: lucas.heublein@iis.fraunhofer.de

Nordostpark 84, 90411 Nürnberg, Germany, GoogleMaps

Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Germany