An End-to-End BLE Indoor Localization Method Using LSTM

• Item type: Journal(1)
• 公開日: 2021-01-15
• タイトル: An End-to-End BLE Indoor Localization Method Using LSTM
• タイトル / 言語: en
• タイトル: An End-to-End BLE Indoor Localization Method Using LSTM
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集：5G時代の社会を創るモバイル・高度交通システム（推薦論文）] location estimation, localization, BLE, deep learning, LSTM, end-to-end location estimation
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Engineering, Nagoya University
• 著者所属: Disaster Prevention Research Institute, Kyoto University
• 著者所属: Graduate School of Engineering, Nagoya University
• 著者所属: Graduate School of Engineering, Nagoya University／Institutes of Innovation for Future Society, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Nagoya University
• 著者所属(英): en
• 著者所属(英): Disaster Prevention Research Institute, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Nagoya University
• 著者所属(英): en
• 著者所属(英): Graduate School of Engineering, Nagoya University / Institutes of Innovation for Future Society, Nagoya University
• 著者名: Kenta, Urano; Kei, Hiroi; Takuro, Yonezawa; Nobuo, Kawaguchi
• 著者名(英): Kenta, Urano; Kei, Hiroi; Takuro, Yonezawa; Nobuo, Kawaguchi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.58; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the difficulty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.58; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 1, 発行日 2021-01-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764