This repository contains the UWB data sets for localizization and NLoS classification. It contains also implementations of localization algorithms, localization error mitigation algorithms and NLoS classification and error regression building code. Ranging error regression and classification algorithms are implemented using TensorFlow framework.
The code needs the following python modules: - numpy - scikit-learn - tensorflow
Installing Python software with Virtualenv can be useful to separate main system Python installation from separate installation with distinctive set of features and libraries installed. This prevents breaking the existing installation of Python and libraries.
$ sudo apt-get install python-pip python-dev python-virtualenv # Python 2.7
$ sudo apt-get install python3-pip python3-dev python-virtualenv # Python 3.n
$ virtualenv --system-site-packages ~/tf # Python 2.7
$ virtualenv --system-site-packages -p python3 ~/tf # Python 3.n
$ source ~/tf/bin/activate
(tf)$ easy_install -U pip
(tf)$ pip install --upgrade tensorflow # Python 2.7
(tf)$ pip3 install --upgrade tensorflow # Python 3
(tf)$ pip install --upgrade tensorflow-gpu # Python 2.7 with gpu support
(tf)$ pip3 install --upgrade tensorflow-gpu # Python 3 with gpu support
If the installation process fails, for more details check the official Tensorflow installation instructions
(tf)$ pip install pandas # Python 2.7
(tf)$ pip3 install pandas # Python 3
(tf)$ pip install sklearn # Python 2.7
(tf)$ pip3 install sklearn # Python 3
(tf)$ pip install scipy # Python 2.7
(tf)$ pip3 install scipy # Python 3
For NLoS classification and ranging error regression models need to be built respectively.
Note: all scripts can be run only in a Python environment, where the Tensorflow is installed. If you installed Tensorflow using Virtualenv (as is presented in this tutorial), the selected tf virtual environment should be activated before running the scripts.
# Inside the folder NLOSClassificationModel run scrip build_classification_model.py
(tf)$ python build_classification_model.py # Python 2.7
(tf)$ python3 build_classification_model.py # Python 3
Note: The process of training the classification model takes around 15 minutes on an average computer.
# Inside the folder RangingErrorModel run script build_regression_model.py
(tf)$ python build_regression_model.py # Python 2.7
(tf)$ python3 build_regression_model.py # Python 3
Note: The process of training the classification model takes around 2.5 hours on an average computer.
To run localization evaluation scripts using classification and error regression techniques to mitigate the effects of ranging errors, check the contents of the folder evaluation.
If you are using our data set in your research, citation of the following paper would be greatly appreciated.
Plain text:
K. Bregar and M. Mohorčič, "Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices," in IEEE Access, vol. 6, pp. 17429-17441, 2018.
doi: 10.1109/ACCESS.2018.2817800
keywords: {Computational modeling;Convolutional neural networks;Distance measurement;Estimation;Heuristic algorithms;Performance evaluation;Prediction algorithms;Channel impulse response;convolutional neural network;deep learning;indoor localization;non-line-of-sight;ranging error mitigation;ultra-wide band},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8320781&isnumber=8274985
BibTeX:
@ARTICLE{8320781,
author={K. Bregar and M. Mohorčič},
journal={IEEE Access},
title={Improving Indoor Localization Using Convolutional Neural Networks on Computationally Restricted Devices},
year={2018},
volume={6},
number={},
pages={17429-17441},
keywords={Computational modeling;Convolutional neural networks;Distance measurement;Estimation;Heuristic algorithms;Performance evaluation;Prediction algorithms;Channel impulse response;convolutional neural network;deep learning;indoor localization;non-line-of-sight;ranging error mitigation;ultra-wide band},
doi={10.1109/ACCESS.2018.2817800},
ISSN={},
month={},}
Author of code and data sets in this repository is Klemen Bregar, [email protected].
See README.md files in individual sub-directories for details.
Copyright (C) 2018 SensorLab, Jožef Stefan Institute http://sensorlab.ijs.si
The research leading to these results has received funding from the European Horizon 2020 Programme project eWINE under grant agreement No. 688116.