TR2020-159

Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation


Abstract:

In addition to coarse-grained received signal strength indicator (RSSI) measurements and fine-grained channel state information (CSI), a mid-grained channel measurement — spatial beam signal-to-noise ratios (SNRs) — that are inherently available during the millimeter wave (mmWave) beam training as defined in mmWave fifth-generation (5G) and IEEE 802.11ad/ay standards, were recently utilized for fingerprintingbased indoor localization. In this paper, we extend the beam SNR fingerprinting-based indoor localization to more challenging scenarios in non-line-of-sight (NLOS) propagation. Particularly, multi-channel beam covariance matrix (BCM) images are used as the fingerprinting signature and fed into a beam covariance learning (BCL) network to identify the position and estimate the coordinate. Using our in-house testbed with commercial off-theshelf (COTS) 60-GHz WiFi routers, real-world mmWave BCMs are fingerprinted in several NLOS locations-of-interest in an enclosed L-shape conference room. Given a fingerprinting gridsize of 30 cm, preliminary performance evaluation shows the position classification accuracy can be above 90% using classical classification methods and a coordinate estimation error around 11 cm with the BCL approach.

 

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  •  Koike-Akino, T., Wang, P., Pajovic, M., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach", IEEE Access, DOI: 10.1109/​ACCESS.2020.2991129, April 2020.
    BibTeX TR2020-054 PDF Data
    • @article{Koike-Akino2020apr,
    • author = {Koike-Akino, Toshiaki and Wang, Pu and Pajovic, Milutin and Sun, Haijian and Orlik, Philip V.},
    • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach},
    • journal = {IEEE Access},
    • year = 2020,
    • month = apr,
    • doi = {10.1109/ACCESS.2020.2991129},
    • issn = {2169-3536},
    • url = {https://www.merl.com/publications/TR2020-054}
    • }
  •  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}
    • }
  •  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}
    • }