| Presentation | 2021-07-29 [Invited Lecture] A Study on Wi-Fi RTT Indoor Positioning Method By Using Machine Learning Ryohei Hayashi, Yuto Nakagawa, Takashi Izumi, Abe Shinya, Yamauchi Hiroki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | As a position estimation method using wireless LAN and Bluetooth, there is a method using Received Signal Strength Indicator (RSSI), but this method is a low positioning accuracy. Another method using Wi-Fi RTT (Round Trip Time) is capable of highly accurate positioning compared to RSSI based one, but the accuracy deteriorates in a multipath environment or NLOS environment. In this paper, we evaluate the positioning performance by machine learning using Wi-Fi RTT. In the experiment, we obtain the self-position and RTT from 6 APs by driving a megarover equipped with SLAM in the office. As a result, we show that the positioning can be performed with an accuracy of 1m or less by using machine learning. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Wi-FI RTT / Indoor positioning / Machine learning / Deep learning |
| Paper # | AP2021-38 |
| Date of Issue | 2021-07-21 (AP) |
| Conference Information | |
| Committee | AP / SANE / SAT |
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| Conference Date | 2021/7/28(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Remote sensing, Sattelite Communication, Radio propagation, Antennas and Propagation |
| Chair | Hiroshi Yamada(Niigata Univ.) / Toshifumi Moriyama(Nagasaki Univ.) / Hiroyasu Ishikawa(Nihon Univ.) |
| Vice Chair | Mitoshi Fujimoto(Fukui Univ) / Makoto Tanaka(Tokai Univ.) / Takeshi Amishima(Mitsubishi Electric) / Tetsushi Ikegami(Meiji Univ.) / Takana Kaho(Shonan Inst. of Tech.) |
| Secretary | Mitoshi Fujimoto(NTT DOCOMO) / Makoto Tanaka(National Defense Academy) / Takeshi Amishima(Univ. of Tokyo) / Tetsushi Ikegami(ENRI) / Takana Kaho(KDDI Research) |
| Assistant | Dr. Kim(Niigata Univ.) / Takayuki Kitamura(Mitsubishi Electric) / Daisuke Goto(NTT) / Yuuki Koizumi(NHK) |
| Paper Information | |
| Registration To | Technical Committee on Antennas and Propagation / Technical Committee on Space, Aeronautical and Navigational Electronics / Technical Committee on Satellite Telecommunications |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Invited Lecture] A Study on Wi-Fi RTT Indoor Positioning Method By Using Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | Wi-FI RTT |
| Keyword(2) | Indoor positioning |
| Keyword(3) | Machine learning |
| Keyword(4) | Deep learning |
| 1st Author's Name | Ryohei Hayashi |
| 1st Author's Affiliation | Panasonic System Networks R&D Lab.Co., Ltd(PSNRD) |
| 2nd Author's Name | Yuto Nakagawa |
| 2nd Author's Affiliation | Panasonic System Networks R&D Lab.Co., Ltd(PSNRD) |
| 3rd Author's Name | Takashi Izumi |
| 3rd Author's Affiliation | Panasonic System Networks R&D Lab.Co., Ltd(PSNRD) |
| 4th Author's Name | Abe Shinya |
| 4th Author's Affiliation | Panasonic(Panasonic) |
| 5th Author's Name | Yamauchi Hiroki |
| 5th Author's Affiliation | Panasonic(Panasonic) |
| Date | 2021-07-29 |
| Paper # | AP2021-38 |
| Volume (vol) | vol.121 |
| Number (no) | AP-126 |
| Page | pp.pp.81-85(AP), |
| #Pages | 5 |
| Date of Issue | 2021-07-21 (AP) |