Greedy EDM FDE

This repository contains code for Greedy Euclidean Distance Matrix-based Fault Detection and Exclusion (FDE) based on the paper "Greedy Detection and Exclusion of Multiple Faults using Euclidean Distance Matrices" by Derek Knowles and Grace Gao from the ION GNSS+ 2023 conference.

Tutorials on how to run EDM FDE using the gnss_lib_py library can be found on the gnss_lib_py documentation website.

Install Dependencies

Install all dependencies with pip install -r requirements.txt

or

Install gnss_lib_py either with pip install gnss_lib_py or following the detailed installation instructions. And install the other needed Python packages in the requirements.txt file.

Run Instructions

ION GNSS+ presentation/paper figures can be replicated through the following:

1. Create the simulated data.

cd greedy-edm-fde/
python3 simulated_data_creation.py

2. Run FDE across the simulated data (takes on the order of hours based on compute).

python3 fde_simulated.py

3. Run FDE across the real-world data (takes on the order of hours based on compute) after updating the train_path_2023 variable in the fde_gsdc.py with your local path to the train directory of the Google Smartphone Decimeter Challenge 2023 dataset.

python3 fde_gsdc.py

4. Create presentation figures by editing the <simulated results directory>, <simulated #>, <gsdc results directory>, and <gsdc #> variables in presentation_figures.py then running the file:

python3 presentation_figures.py

Citation

If referencing greedy EDM FDE in your work, please cite the following paper:

@inproceedings{Knowles2023,
author = {Knowles, Derek and Gao, Grace},
title = {{Detection and Exclusion of Multiple Faults using Euclidean Distance Matrices}},
booktitle = {Proceedings of the 36th International Technical Meeting of the Satellite Divison of the Institute of Navigation, ION GNSS + 2023},
publisher = {Institute of Navigation},
year = {2023}
}