TR2019-141

Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part I: RSS and Beam Indices

Abstract:

Millimeter-wave (mmWave) communications is an emerging technology expected to bring unprecedented data rates and throughput. WiFi operating at unlicensed 60 GHz range is envisioned to become an ubiquitous technology and the IEEE 802.11ad standard is an initial attempt in that direction. Although spatial and temporal resolution of mmWave signals make them suitable for location estimation, a variety of hardwarerelated issues and commonly encountered difficulties in extracting channel measurements from commercial chipsets, challenge opportunistic use of commercial mmWave WiFi chips for indoor localization. We propose in this paper an indoor localization method that fingerprints transmit beam indices that a pair of WiFi transceivers employ to establish a mmWave link, as well as the resulting received signal strength (RSS). In particular, we develop an algorithm that learns possible probabilistic models from the fingerprint data and leverages them to perform indoor localization in the online stage. The proposed algorithm is experimentally evaluated using commercial 60 GHz WiFi routers in an office space

 

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    •  NEWS    MERL Scientists Presenting 11 Papers at IEEE Global Communications Conference (GLOBECOM) 2019
      Date: December 9, 2019 - December 13, 2019
      Where: Waikoloa, Hawaii, USA
      MERL Contacts: Jianlin Guo; Toshiaki Koike-Akino; Philip V. Orlik; Pu (Perry) Wang
      Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information Security
      Brief
      • MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.

        GLOBECOM is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 3000 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. Themed “Revolutionizing Communications,” GLOBECOM2019 will feature a comprehensive high-quality technical program including 13 symposia and a variety of tutorials and workshops to share visions and ideas, obtain updates on latest technologies and expand professional and social networking.
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  • Related Research Highlights

  • Related Publications

  •  Wang, P., Koike-Akino, T., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/​GLOBECOM42002.2020.9348144, December 2020.
    BibTeX TR2020-159 PDF
    • @inproceedings{Wang2020dec,
    • author = {Wang, Pu and Koike-Akino, Toshiaki and Orlik, Philip V.},
    • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation},
    • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
    • year = 2020,
    • month = dec,
    • publisher = {IEEE},
    • doi = {10.1109/GLOBECOM42002.2020.9348144},
    • issn = {2576-6813},
    • isbn = {978-1-7281-8298-8},
    • url = {https://www.merl.com/publications/TR2020-159}
    • }
  •  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
    • @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}
    • }
  •  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
    • @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}
    • }