Preprint Article Version 2 This version is not peer-reviewed

A Deep Learning Approach for Wi-Fi Based People Localization

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. Preprints 2018, 2018090213 (doi: 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 (doi: 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.

Subject Areas

deep learning; compressive sensing; people localization; signal detection; Wi-Fi

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