IEICE TRANSACTIONS on Fundamentals
Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data
Summary :
This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a twodimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.5 pp.778-786
- Publication Date
- 2022/05/01
- Publicized
- 2021/11/01
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021WBP0007
- Type of Manuscript
- Special Section PAPER (Special Section on Intelligent Transport Systems and Wideband Systems for Smart Cities and Mobility)
- Category
Authors
Tetsuya MANABE
Saitama University
Kosuke OMURA
Saitama University
Keyword
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Tetsuya MANABE, Kosuke OMURA, "Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 5, pp. 778-786, May 2022, doi: 10.1587/transfun.2021WBP0007.
Abstract: This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a twodimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021WBP0007/_p
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@ARTICLE{e105-a_5_778,
author={Tetsuya MANABE, Kosuke OMURA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data},
year={2022},
volume={E105-A},
number={5},
pages={778-786},
abstract={This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a twodimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.},
keywords={},
doi={10.1587/transfun.2021WBP0007},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 778
EP - 786
AU - Tetsuya MANABE
AU - Kosuke OMURA
PY - 2022
DO - 10.1587/transfun.2021WBP0007
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2022
AB - This paper evaluates the bluetooth low energy (BLE) positioning systems using the sparse-training data through the comparison experiments. The sparse-training data is extracted from the database including enough data for realizing the highly accurate and precise positioning. First, we define the sparse-training data, i.e., the data collection time and the number of smartphones, directions, beacons, and reference points, on BLE positioning systems. Next, the positioning performance evaluation experiments are conducted in two indoor environments, that is, an indoor corridor as a one-dimensionally spread environment and a hall as a twodimensionally spread environment. The algorithms for comparison are the conventional fingerprint algorithm and the hybrid algorithm (the authors already proposed, and combined the proximity algorithm and the fingerprint algorithm). Based on the results, we confirm that the hybrid algorithm performs well in many cases even when using sparse-training data. Consequently, the robustness of the hybrid algorithm, that the authors already proposed for the sparse-training data, is shown.
ER -
English
