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.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509041831 |
| DOIs | |
| State | Published - 10 May 2017 |
| Event | 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 - San Francisco, United States Duration: 19 Mar 2017 → 22 Mar 2017 |
Publication series
| Name | IEEE Wireless Communications and Networking Conference, WCNC |
|---|---|
| ISSN (Print) | 1525-3511 |
Conference
| Conference | 2017 IEEE Wireless Communications and Networking Conference, WCNC 2017 |
|---|---|
| Country/Territory | United States |
| City | San Francisco |
| Period | 19/03/17 → 22/03/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.
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