Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement - spatial be...

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Bibliographic Details
Published inIEEE access Vol. 8; pp. 84879 - 84892
Main Authors Koike-Akino, Toshiaki, Wang, Pu, Pajovic, Milutin, Sun, Haijian, Orlik, Philip V.
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Classification
coordinate estimation
Deep learning
deep neural networks
Fingerprinting
Indoor localization
Localization
location
Machine learning
millimeter wave
Millimeter waves
Noise measurement
orientation
Root-mean-square errors
Routers
Signal strength
Signal to noise ratio
WiFi
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Summary:Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standards - to construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous location-and-orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and-orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2991129