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Device-Free Indoor Localization Using Wi-Fi Channel State Information for Internet of Things

Published on Dec 1, 2018 in GLOBECOM (Global Communications Conference)
· DOI :10.1109/glocom.2018.8647261
Ronald Y. Chang12
Estimated H-index: 12
(CIT: Center for Information Technology),
Shing-Jiuan Liu1
Estimated H-index: 1
(CIT: Center for Information Technology),
Yen-Kai Cheng2
Estimated H-index: 2
(CIT: Center for Information Technology)
Abstract
This paper proposes an economical, nonintrusive, and high-precision indoor localization scheme based on Wi-Fi fingerprinting that requires only a single Wi- Fi access point and a single fixed-location receiver. A deep neural network (DNN) based classification model is trained with Wi-Fi channel state information (CSI) fingerprints for localizing the target without any device attached (i.e., device-free). CSI provides finer-grained information than received signal strength (RSS). CSI pre- processing based on singular value decomposition (SVD), as well as data augmentation based on noise injection and inter-person interpolation, are incorporated into the proposed DNN framework for enhanced robustness and performance. Real-world experiments examine two scenarios with different degrees of target similarity and show that the proposed DNN-based system can consistently improve the localization performance as compared to the original DNN model.
  • References (24)
  • Citations (1)
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In this paper we introduce a novel device-free radio based activity recognition with localization method with various applications, such as e-Healthcare and security. Our method uses the properties of the signal subspace, which are estimated using signal eigenvectors of the covariance matrix obtained from an antenna array (array sensor) at the receiver side. To classify human activities (e.g., standing and moving) and/or positions, we apply a machine learning method with support vector machines ...
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Cited By1
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Dec 1, 2019 in GLOBECOM (Global Communications Conference)
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Device-free indoor localization is a key enabling technology for many Internet of Things (IoT) applications. Deep neural network (DNN)-based location estimators achieve high-precision localization performance by automatically learning discriminative features from noisy wireless signals without much human intervention. However, the inner workings of DNN are not transparent and not adequately understood especially in wireless localization applications. In this paper, we conduct visual analyses of ...
Source
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Device-free Wi-Fi indoor localization has received significant attention as a key enabling technology for many Internet of Things (IoT) applications. Machine learning-based location estimators, such as the deep neural network (DNN), carry proven potential in achieving high-precision localization performance by automatically learning discriminative features from the noisy wireless signal measurements. However, the inner workings of the DNNs are not transparent and not adequately understood, espec...
Source