Journal of Information Processing
Online ISSN : 1882-6652
ISSN-L : 1882-6652
An End-to-End BLE Indoor Localization Method Using LSTM
Kenta UranoKei HiroiTakuro YonezawaNobuo Kawaguchi
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2021 Volume 29 Pages 58-69

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Abstract

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.

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© 2021 by the Information Processing Society of Japan
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