DeepMTL: Deep Learning Based Multiple Transmitter Localization.

The YOLOv3 part of the paper is in this repo: https://github.com/caitaozhan/PyTorch-YOLOv3

This is the repository of my IEEE WoWMoM 2021 paper: IEEE Link, Open Access PDF link.

The WoWMoM conference presentation is on my YouTube channel.

The WoWMoM conference paper is extended to the Elsevier journal Pervasive and Mobile Computing (PMC): Elsevier Link, Open Access PDF link. The Elsevier version is also uploaded in this repository, checkout deepmtl-pro.pdf. The main new part is the transmit power estimation. Also the quality of writing is improved.

I also made a presentation for the PMC journal paper, plese check my YouTube channel.

Please cite the DeepMTL conference paper:

@INPROCEEDINGS{wowmom21-deepmtl,
title={DeepMTL: Deep Learning Based Multiple Transmitter Localization}, 
booktitle={IEEE 22nd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)}, 
author={Zhan, Caitao and Ghaderibaneh, Mohammad and Sahu, Pranjal and Gupta, Himanshu},
year={2021},
doi={10.1109/WoWMoM51794.2021.00017}
}

Please cite the DeepMTL Pro journal paper:

@article{pmc22-deepmtlpro,
title = {DeepMTL Pro: Deep Learning Based Multiple Transmitter Localization and Power Estimation},
author = {Caitao Zhan and Mohammad Ghaderibaneh and Pranjal Sahu and Himanshu Gupta},
journal = {Pervasive and Mobile Computing},
year = {2022},
doi={10.1016/j.pmcj.2022.101582}
}