Spatial skeleton-enhanced location tracking for indoor localization

Chun Jie Chiu; Kai Ten Feng; Po Hsuan Tseng

doi:10.1109/WCNC.2017.7925626

Spatial skeleton-enhanced location tracking for indoor localization

Chun Jie Chiu
, Kai Ten Feng
, Po Hsuan Tseng

電子工程系

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

5 引文斯高帕斯（Scopus）

摘要

Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.

原文	English
主出版物標題	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781509041831
DOIs	https://doi.org/10.1109/WCNC.2017.7925626
出版狀態	Published - 10 5月 2017
事件	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - San Francisco, United States 持續時間: 19 3月 2017 → 22 3月 2017

出版系列

名字	IEEE Wireless Communications and Networking Conference, WCNC
ISSN（列印）	1525-3511

Conference

Conference	2017 IEEE Wireless Communications and Networking Conference, WCNC 2017
國家/地區	United States
城市	San Francisco
期間	19/03/17 → 22/03/17

文獻附註

Publisher Copyright:
© 2017 IEEE.

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10.1109/WCNC.2017.7925626

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Link to publication in Scopus

引用此

Chiu, C. J., Feng, K. T., & Tseng, P. H. (2017). Spatial skeleton-enhanced location tracking for indoor localization. 於 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings 文章 7925626 (IEEE Wireless Communications and Networking Conference, WCNC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC.2017.7925626

@inproceedings{55c4fc85c4664431b811adbb408f8af2,

title = "Spatial skeleton-enhanced location tracking for indoor localization",

abstract = "Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.",

author = "Chiu, \{Chun Jie\} and Feng, \{Kai Ten\} and Tseng, \{Po Hsuan\}",

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Chiu, CJ, Feng, KT & Tseng, PH 2017, Spatial skeleton-enhanced location tracking for indoor localization. 於 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings., 7925626, IEEE Wireless Communications and Networking Conference, WCNC, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017, San Francisco, United States, 19/03/17. https://doi.org/10.1109/WCNC.2017.7925626

Spatial skeleton-enhanced location tracking for indoor localization. / Chiu, Chun Jie; Feng, Kai Ten; Tseng, Po Hsuan.
2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. 7925626 (IEEE Wireless Communications and Networking Conference, WCNC).

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

TY - GEN

T1 - Spatial skeleton-enhanced location tracking for indoor localization

AU - Chiu, Chun Jie

AU - Feng, Kai Ten

AU - Tseng, Po Hsuan

PY - 2017/5/10

Y1 - 2017/5/10

N2 - Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.

AB - Map information can assist indoor localization to avoid improbable cases and achieve accurate location estimation. In this paper, we proposed a automatic method to extract useful information from indoor map as spatial skeleton database (SSD). Based on conventional probabilistic fingerprinting technique and particle filter tracking algorithm, we also proposed spatial skeleton-based dynamic probabilistic fingerprinting database (S-DFD) to filter out reference points (RPs) in fingerprinting database according to the previous target location and the walking distance between RPs. Finally, we proposed a spatial skeleton-based particle filter tracking (S-PT) which use SSD to construct realistic transition model. According to the experiment result, the whole system consists of SSD, S-DFD and S-PT called spatial skeleton-enhanced location tracking for indoor localization (SELT) can achieve accurate location estimation.

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Spatial skeleton-enhanced location tracking for indoor localization

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