An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

Chun Han Lin; Lyu Han Chen; Hsiao Kuang Wu; Ming Hui Jin; Gen Huey Chen; Jose Luis Garcia Gomez; Cheng Fu Chou

doi:10.1109/TSMC.2019.2917955

An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

Chun Han Lin, Lyu Han Chen, Hsiao Kuang Wu, Ming Hui Jin, Gen Huey Chen, Jose Luis Garcia Gomez, Cheng Fu Chou

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

The creation of context-Aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

Original language	English
Article number	8732597
Pages (from-to)	2926-2936
Number of pages	11
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	51
Issue number	5
DOIs	https://doi.org/10.1109/TSMC.2019.2917955
State	Published - May 2021

Keywords

Fingerprint
indoor positioning
mobility prediction
received signal strength (RSS)
wireless local area network (WLAN)

Access to Document

10.1109/TSMC.2019.2917955

Cite this

@article{00e54c8fdd9945459080c090c43ddb90,

title = "An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs",

abstract = "The creation of context-Aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.",

keywords = "Fingerprint, indoor positioning, mobility prediction, received signal strength (RSS), wireless local area network (WLAN)",

author = "Lin, {Chun Han} and Chen, {Lyu Han} and Wu, {Hsiao Kuang} and Jin, {Ming Hui} and Chen, {Gen Huey} and {Garcia Gomez}, {Jose Luis} and Chou, {Cheng Fu}",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2021",

month = may,

doi = "10.1109/TSMC.2019.2917955",

language = "???core.languages.en_GB???",

volume = "51",

pages = "2926--2936",

journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",

issn = "2168-2216",

number = "5",

}

TY - JOUR

T1 - An Indoor Positioning Algorithm Based on Fingerprint and Mobility Prediction in RSS Fluctuation-Prone WLANs

AU - Lin, Chun Han

AU - Chen, Lyu Han

AU - Wu, Hsiao Kuang

AU - Jin, Ming Hui

AU - Chen, Gen Huey

AU - Garcia Gomez, Jose Luis

AU - Chou, Cheng Fu

PY - 2021/5

Y1 - 2021/5

N2 - The creation of context-Aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

AB - The creation of context-Aware services in pervasive computing environments has driven the wide development of wireless local area network (WLAN)-based indoor positioning systems. One of the main challenges in WLAN-based indoor positioning is the severe fluctuation of received signal strength (RSS), which may cause the RSS patterns to be mismatched and the positioning to be inaccurate. In this paper, an indoor positioning algorithm that combines the fingerprint scheme with mobility prediction is proposed. Since the mobility prediction is performed according to the moving speed and direction of the mobile client, the resulting location estimation is more stable compared to the use of RSS alone. Experimental results show that the proposed positioning algorithm can mitigate the impact of the RSS fluctuation and has better positioning accuracy and stability than previous fingerprint-based approaches.

KW - Fingerprint

KW - indoor positioning

KW - mobility prediction

KW - received signal strength (RSS)

KW - wireless local area network (WLAN)

UR - http://www.scopus.com/inward/record.url?scp=85104340174&partnerID=8YFLogxK

U2 - 10.1109/TSMC.2019.2917955

DO - 10.1109/TSMC.2019.2917955

M3 - 期刊論文

AN - SCOPUS:85104340174

SN - 2168-2216

VL - 51

SP - 2926

EP - 2936

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

IS - 5

M1 - 8732597

ER -