In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.
Myat Hsu AUNG
Hokkaido University
Hiroshi TSUTSUI
Hokkaido University
Yoshikazu MIYANAGA
Hokkaido University
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Myat Hsu AUNG, Hiroshi TSUTSUI, Yoshikazu MIYANAGA, "WiFi Fingerprint Based Indoor Positioning Systems Using Estimated Reference Locations" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 12, pp. 1483-1493, December 2020, doi: 10.1587/transfun.2020SMP0024.
Abstract: In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020SMP0024/_p
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@ARTICLE{e103-a_12_1483,
author={Myat Hsu AUNG, Hiroshi TSUTSUI, Yoshikazu MIYANAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={WiFi Fingerprint Based Indoor Positioning Systems Using Estimated Reference Locations},
year={2020},
volume={E103-A},
number={12},
pages={1483-1493},
abstract={In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.},
keywords={},
doi={10.1587/transfun.2020SMP0024},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - WiFi Fingerprint Based Indoor Positioning Systems Using Estimated Reference Locations
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1483
EP - 1493
AU - Myat Hsu AUNG
AU - Hiroshi TSUTSUI
AU - Yoshikazu MIYANAGA
PY - 2020
DO - 10.1587/transfun.2020SMP0024
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2020
AB - In this paper, we propose a WiFi-based indoor positioning system using a fingerprint method, whose database is constructed with estimated reference locations. The reference locations and their information, called data sets in this paper, are obtained by moving reference devices at a constant speed while gathering information of available access points (APs). In this approach, the reference locations can be estimated using the velocity without any precise reference location information. Therefore, the cost of database construction can be dramatically reduced. However, each data set includes some errors due to such as the fluctuation of received signal strength indicator (RSSI) values, the device-specific WiFi sensitivities, the AP installations, and removals. In this paper, we propose a method to merge data sets to construct a consistent database suppressing such undesired effects. The proposed approach assumes that the intervals of reference locations in the database are constant and that the fingerprint for each reference location is calculated from multiple data sets. Through experimental results, we reveal that our approach can achieve an accuracy of 80%. We also show a detailed discussion on the results related parameters in the proposed approach.
ER -