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- Urano Kenta
- Graduate School of Engineering, Nagoya University
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- Hiroi Kei
- Disaster Prevention Research Institute, Kyoto University
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- Yonezawa Takuro
- Graduate School of Engineering, Nagoya University
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- Kawaguchi Nobuo
- Graduate School of Engineering, Nagoya University Institutes of Innovation for Future Society, Nagoya University
抄録
<p>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.</p>
収録刊行物
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- Journal of Information Processing
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Journal of Information Processing 29 (0), 58-69, 2021
一般社団法人 情報処理学会
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詳細情報 詳細情報について
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- CRID
- 1391694356259011840
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- NII論文ID
- 130007969005
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- ISSN
- 18826652
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可