Version 1
: Received: 12 September 2018 / Approved: 12 September 2018 / Online: 12 September 2018 (05:59:51 CEST)
Version 2
: Received: 6 November 2018 / Approved: 7 November 2018 / Online: 7 November 2018 (07:37:38 CET)
How to cite:
Khalili, A.M.; Soliman, A.; Asaduzzaman, M. A Deep Learning Approach for Wi-Fi Based People Localization. Preprints2018, 2018090213. https://doi.org/10.20944/preprints201809.0213.v2
Khalili, A.M.; Soliman, A.; Asaduzzaman, M. A Deep Learning Approach for Wi-Fi Based People Localization. Preprints 2018, 2018090213. https://doi.org/10.20944/preprints201809.0213.v2
Khalili, A.M.; Soliman, A.; Asaduzzaman, M. A Deep Learning Approach for Wi-Fi Based People Localization. Preprints2018, 2018090213. https://doi.org/10.20944/preprints201809.0213.v2
APA Style
Khalili, A.M., Soliman, A., & Asaduzzaman, M. (2018). A Deep Learning Approach for Wi-Fi Based People Localization. Preprints. https://doi.org/10.20944/preprints201809.0213.v2
Chicago/Turabian Style
Khalili, A.M., Abdel-Hamid Soliman and Md Asaduzzaman. 2018 "A Deep Learning Approach for Wi-Fi Based People Localization" Preprints. https://doi.org/10.20944/preprints201809.0213.v2
Abstract
People localization is a key building block in many applications. In this paper, we propose a deep learning based approach that significantly improves the localization accuracy and reduces the runtime of Wi-Fi based localization systems. Three variants of the deep learning approach are proposed, a sub-task architecture, an end-to-end architecture, and an architecture that incorporates prior knowledge. The performance of the three architectures under different conditions is evaluated and the significant improvement of the three architectures over existing approaches is demonstrated.
Keywords
deep learning; compressive sensing; people localization; signal detection; Wi-Fi
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.