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Performance Evaluation of Bluetooth Low Energy Positioning Systems When Using Sparse Training Data

Tetsuya MANABE, Kosuke OMURA

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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