| Presentation | 2022-11-07 Neural network configuration of machine learning for location estimation at various altitudes using multiple items of sensed information in indoor environment Ren Kawamura, Eisuke Kudoh, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In an indoor environment, it is difficult to receive radio waves directly from satellites which hinders accurate location estimation by satellite signals. Meanwhile, mobile communication propagation channels suffer from fading and shadowing, so estimation of indoor locations by using only the received signal power of a radio wave is inaccurate as well. Sensed information (e.g., temperature, humidity, illuminance) is often location dependent, and a location can be estimated accurately if such information is used in addition to the received signal power. The use of machine learning for an optimization algorithm has been shown to be promising. In this paper, we apply machine learning for indoor location estimation at various altitudes using multiple items of sensed information. We propose two types of neural networks, a two-dimensional neural network and a nearest node neural network, and experimentally evaluate them in an actual building. The results indicate that location estimation using the nearest node neural network has a greater coincidence probability than that using the two-dimensional neural network. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | location estimation / machine learning / ZigBee / sensed information / IoT |
| Paper # | SR2022-45 |
| Date of Issue | 2022-10-31 (SR) |
| Conference Information | |
| Committee | SR |
|---|---|
| Conference Date | 2022/11/7(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Fukuoka University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Defined Radio, Cognitive Radio, Spectrum Sharing, etc. |
| Chair | Suguru Kameda(Hiroshima Univ.) |
| Vice Chair | Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Kazuto Yano(ATR) |
| Secretary | Osamu Takyu(Mie Univ.) / Kentaro Ishidu(Tokai Univ.) / Kazuto Yano(NTT) |
| Assistant | Taichi Ohtsuji(NEC) / WANG Xiaoyan(Ibaraki Univ.) / Akemi Tanaka(MathWorks) / Katsuya Suto(Univ. of Electro-Comm) |
| Paper Information | |
| Registration To | Technical Committee on Smart Radio |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Neural network configuration of machine learning for location estimation at various altitudes using multiple items of sensed information in indoor environment |
| Sub Title (in English) | |
| Keyword(1) | location estimation |
| Keyword(2) | machine learning |
| Keyword(3) | ZigBee |
| Keyword(4) | sensed information |
| Keyword(5) | IoT |
| 1st Author's Name | Ren Kawamura |
| 1st Author's Affiliation | Tohoku Institute of Technology(Tohoku Institute of Tech.) |
| 2nd Author's Name | Eisuke Kudoh |
| 2nd Author's Affiliation | Tohoku Institute of Technology(Tohoku Institute of Tech.) |
| Date | 2022-11-07 |
| Paper # | SR2022-45 |
| Volume (vol) | vol.122 |
| Number (no) | SR-243 |
| Page | pp.pp.1-6(SR), |
| #Pages | 6 |
| Date of Issue | 2022-10-31 (SR) |