TR2020-054

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 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 locationand 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

 

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  •  Wang, P., Pajovic, M., Koike-Akino, T., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part II: Spatial Beam SNRs", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GLOBECOM38437.2019.9014103, December 2019.
    BibTeX TR2019-138 PDF Data
    • @inproceedings{Wang2019dec2,
    • author = {Wang, Pu and Pajovic, Milutin and Koike-Akino, Toshiaki and Sun, Haijian and Orlik, Philip V.},
    • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part II: Spatial Beam SNRs},
    • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
    • year = 2019,
    • month = dec,
    • publisher = {IEEE},
    • doi = {10.1109/GLOBECOM38437.2019.9014103},
    • issn = {2576-6813},
    • isbn = {978-1-7281-0962-6},
    • url = {https://www.merl.com/publications/TR2019-138}
    • }
  •  Pajovic, M., Wang, P., Koike-Akino, T., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part I: RSS and Beam Indices", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GLOBECOM38437.2019.9013466, December 2019.
    BibTeX TR2019-141 PDF Data
    • @inproceedings{Pajovic2019dec,
    • author = {Pajovic, Milutin and Wang, Pu and Koike-Akino, Toshiaki and Sun, Haijian and Orlik, Philip V.},
    • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part I: RSS and Beam Indices},
    • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
    • year = 2019,
    • month = dec,
    • publisher = {IEEE},
    • doi = {10.1109/GLOBECOM38437.2019.9013466},
    • issn = {2576-6813},
    • isbn = {978-1-7281-0962-6},
    • url = {https://www.merl.com/publications/TR2019-141}
    • }