This folder holds Python scripts with localization algorithms, data set importing tools, classification and regression tools.
This file contains the NLoS classification class that needs prebuild classification model from the NLOSClassificationModel folder. Model needs to be built before using the NLoS classification feature.
This file holds methods and classes that load and preformat the data for particular evaluations and training sessions.
This file holds methods for localization evaluation node selections and returns the estimated location for selected methos.
This file holds the basic multilateration algorithms used in evaluation scripts through localization.py
This file holds the ranging error regression class used in evaluation scripts.
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 demonstration and evaluation scripts is Klemen Bregar, klemen.bregar@ijs.si.
Copyright (C) 2018 SensorLab, Jožef Stefan Institute http://sensorlab.ijs.si
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses