FelixOtt/gnss_cross_validation

Cross-Validation of all Spectrum Datasets for Interference Classification in GNSS Data

Latest update: 2024-10-30

Project size: 11 GB

Introduction

We publish our source code to train and test a ResNet18 machine learning model to cross-validation all GNSS interference classification datasets. The train and test splits are defined in the corresponding .txt files.

Datasets

The datasets con be downloaded from the following websites:

For our spectrum highway dataset 1 recorded along a highway bridge, see the following website: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway

For our spectrum highway dataset 2 recorded along a 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

Train/test splits

1. train_resnet.py: train a model

   --run_num defines the number of the training run (important for uncertainty_calculation)

   --data defines the dataset which is used example: 0,1 will train the highway dataset 1 and highway dataset 2 combined (integers seperated with ,)

   --augment defines whether to use augmentation or not (integer 0 or 1)

2. test_resnet.py: test a trained model on all test datasets

   --folder defines the string name of the folder in which the training to be tested is stored (str, example: resnet for the folder results_resnet; resnet_augment for the folder results_resnet_augment)

   --run_num defines the run number of the training to be tested (integer)

3. collect_results.py: collect all results from a defined folder

   --folder defines the string name of the folder in which the test results to be colected are (str, example: resnet for the folder results_resnet; resnet_augment for the folder results_resnet_augment), collected results will be stored in the provided folder + "/results/"

4. uncertainty.py: calculate the uncertainty from trainings of a given folder (Each training has to be done multiple times with different seeds and all those need consecutive run_numbers)

   --folder defines the string name of the folder in which the predictions for the uncertainty are (str, example: resnet for the folder results_resnet; resnet_augment for the folder results_resnet_augment)

   --repeats defines the number of repeats each training was trained for (traings done with the same dataset, but different random seeds, need to have run_nums in consecutive order)(integer, example: 10 if each training was done with 10 different random seeds) uncertainties will be stored in the provided folder + "/uncertainties/"

5. plot_results.py: creates two boxplots showing the F2-scores and accuracies of each dataset and other plots showing the confusion matrices and uncertainties of each dataset

   --folder defines the string name of the folder in which the results are (str, example: resnet for the folder results_resnet; resnet_augment for the folder results_resnet_augment)

   --repeats defines the number of repeats each training was trained for (traings done with the same dataset, but different random seeds, need to have run_nums in consecutive order)(integer, example: 10 if each training was done with 10 different random seeds) images will be stored in the provided folder + "/images/"

References

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

@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}
}

For more information on the datasets, see:

@inproceedings{heublein_feigl_dataset,
  author = {Lucas Heublein and Tobias Feigl and Thorsten Nowak and Alexander Rügamer and Christopher Mutschler and Felix Ott},
  title = {{Evaluating ML Robustness in GNSS Interference Classification, Characterization \& Localization}},
  booktitle = {\href{https://arxiv.org/abs/2409.15114}{arXiv preprint arXiv:2409.15114}},
  month = sep,
  year = {2024}
}

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