Name
Abstract | ||
|---|---|---|
In this paper, LSTM-based neural network is applied to indoor localization using mobile BLE tag's signal strength collected by multiple scanners. Stability of signal strength is a critical factor of wireless indoor localization for higher accuracy. While traditional methods like trilateration and fingerprinting suffer from noise and packet loss, deep learning based methods perform well. We focus on large-scale exhibition where wireless signal gets unstable due to many people. Proposed neural network consists of fully connected layers for noise removal and LSTM layers for time-series feature extraction. The network takes the time-series of signal strength as input and outputs the estimated location. In the evaluation, the number of layers is changed to find the optimal structure. As a result, the best configuration achieved the error of 2.44m at 75 percentile for the data of a large-scale exhibition in Tokyo.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3307334.3328624 | Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services |
Keywords | DocType | ISBN |
ble, deep learning, indoor localization, lstm | Conference | 978-1-4503-6661-8 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenta Urano | 1 | 8 | 4.17 |
| Kei Hiroi | 2 | 19 | 12.00 |
| Takuro Yonezawa | 3 | 84 | 22.34 |
| Nobuo Kawaguchi | 4 | 313 | 64.23 |