Passive Indoor Localization with WiFi Fingerprints
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866914128181854208 |
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| author | Hoang, Minh Tu Yuen, Brosnan Ren, Kai Elmoogy, Ahmed Dong, Xiaodai Lu, Tao Westendorp, Hung Le Nguyen Robert Tarimala, Kishore Reddy |
| author_facet | Hoang, Minh Tu Yuen, Brosnan Ren, Kai Elmoogy, Ahmed Dong, Xiaodai Lu, Tao Westendorp, Hung Le Nguyen Robert Tarimala, Kishore Reddy |
| contents | This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of 0.8 m when the phone is actively transmitting data frames and 1.5 m when it is not transmitting data frames. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2111_14281 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Passive Indoor Localization with WiFi Fingerprints Hoang, Minh Tu Yuen, Brosnan Ren, Kai Elmoogy, Ahmed Dong, Xiaodai Lu, Tao Westendorp, Hung Le Nguyen Robert Tarimala, Kishore Reddy Signal Processing Networking and Internet Architecture This paper proposes passive WiFi indoor localization. Instead of using WiFi signals received by mobile devices as fingerprints, we use signals received by routers to locate the mobile carrier. Consequently, software installation on the mobile device is not required. To resolve the data insufficiency problem, flow control signals such as request to send (RTS) and clear to send (CTS) are utilized. In our model, received signal strength indicator (RSSI) and channel state information (CSI) are used as fingerprints for several algorithms, including deterministic, probabilistic and neural networks localization algorithms. We further investigated localization algorithms performance through extensive on-site experiments with various models of phones at hundreds of testing locations. We demonstrate that our passive scheme achieves an average localization error of 0.8 m when the phone is actively transmitting data frames and 1.5 m when it is not transmitting data frames. |
| title | Passive Indoor Localization with WiFi Fingerprints |
| topic | Signal Processing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2111.14281 |