TR2020-159
Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation
-
- "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation", IEEE Global Communications Conference (GLOBECOM), 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,
- url = {https://www.merl.com/publications/TR2020-159}
- }
,
- "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation", IEEE Global Communications Conference (GLOBECOM), December 2020.
-
MERL Contacts:
-
Research Areas:
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.
Related News & Events
-
NEWS MERL published four papers in 2020 IEEE Global Communications Conference Date: December 7, 2020 - December 11, 2020
Where: Taipei, Taiwan
MERL Contacts: Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Philip Orlik; Pu (Perry) Wang; Ye Wang
Research Areas: Communications, Computational Sensing, Machine Learning, Signal ProcessingBrief- MERL researchers have published four papers in 2020 IEEE Global Communications Conference (GlobeComm). This conference is one of the two IEEE Communications Societies flagship conferences dedicated to Communications for Human and Machine Intelligence. Topics of the published papers include, transmit diversity schemes, coding for molecular networks, and location and human activity sensing via WiFi signals.
Related Publications
- @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}
- }
- @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}
- }
- @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}
- }