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Open AccessFeature PaperArticle

SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

1
Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802, USA
2
Applied Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1323; https://doi.org/10.3390/electronics8111323 (registering DOI)
Received: 28 October 2019 / Revised: 6 November 2019 / Accepted: 7 November 2019 / Published: 10 November 2019
(This article belongs to the Special Issue Indoor Localization: Technologies and Challenges)
Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.
Keywords: beaconing; deep learning; denoising autoencoder; indoor positioning; multipath channel estimation; polarization diversity; vector sensor beaconing; deep learning; denoising autoencoder; indoor positioning; multipath channel estimation; polarization diversity; vector sensor
MDPI and ACS Style

Hall, D.L.; Narayanan, R.M.; Jenkins, D.M. SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning. Electronics 2019, 8, 1323.

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