GPMS: Enabling Indoor GNSS Positioning using Passive Metasurfaces
Global Navigation Satellite System (GNSS) is extensively utilized for outdoor positioning and navigation. However, achieving high-precision indoor positioning is challenging due to the significant attenuation of GNSS signals indoors. To address this issue, we propose an innovative indoor GNSS positioning system called GPMS, which uses passive metasurface technology to redirect GNSS signals from outdoors into indoor spaces. These passive metasurfaces are strategically optimized for indoor coverage by steering and scattering the GNSS signals across a wide range of incident angles. We further develop a novel localization algorithm that can determine which metasurface the signal goes through and localize the user using the set of metasurfaces as anchor points. A distinct advantage of our localization algorithm is that it can be implemented on existing mobile devices without any hardware modifications. We implement the prototype of GPMS, and deploy six metasurfaces in two indoor environments, a 10×50 m2 office floor and a 15×20 m2 lecture room, to evaluate system performance. In terms of coverage, our GPMS increases the C/N0 from 9.1 dB-Hz to 23.2 dB-Hz and increases the number of visible satellites from 3.6 to 21.5 in the office floor. In terms of indoor positioning accuracy, our proposed system decreases the absolute positioning error from 30.6 m to 3.2 m in the office floor, and from 11.2 m to 2.7 m in the lecture room, demonstrating the feasibility and benefits of metasurface-assisted GNSS for indoor positioning.
Authors
- Jiting Liu
- Yi‐Chao Chen (ORCID: https://orcid.org/0000-0003-0782-4953)
- Ju Ren (ORCID: https://orcid.org/0000-0003-2782-183X)
- Lili Qiu
- Ruichun Ma (ORCID: https://orcid.org/0009-0001-0832-569X)
- Yezhou Wang (ORCID: https://orcid.org/0000-0003-0726-9398)
- Linghui Zhong (ORCID: https://orcid.org/0000-0003-2178-5073)
- Hao Pan (ORCID: https://orcid.org/0000-0002-2531-0107)
- Guangtao Xue (ORCID: https://orcid.org/0000-0002-1617-3593)
Institutions
- Central South University (CN)
- Shanghai Jiao Tong University (CN)
- Yale University (US)
- Microsoft Research Asia (China) (CN)
- Columbia University (US)
- The University of Texas at Austin (US)
- Tsinghua University (CN)
Publication Details
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1145/3636534.3690702
- Citations
- 9
- Primary Topic
- Indoor and Outdoor Localization Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- National Natural Science Foundation of China