期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 3, 页码 898-908出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2750240
关键词
Fingerprint-based technique; indoor localization; transfer learning
类别
资金
- National Science Foundation of China [61572088, 61603064]
- Chongqing application foundation and research in cutting-edge technologies [cstc2017jcyjAX0026]
- Frontier Interdisciplinary Research Fund for the Central Universities [106112017CD-JQJ188828]
- HKBU Research Centre for Ubiquitous Computing
- HKBU Institute of Computational and Theoretical Studies
- Innovation and Technology Commission of the HK SAR Government under the Innovation and Technology Fund [ITP/048/14LP]
- ICT R&D program of MSIP/IITP [14-824-09-013]
- GRL Program through NRF [2013K1A1A2A02078326]
- DGIST Research and Development Program (CPS Global Center) - Ministry of Science, ICT and Future Planning
- National Research Foundation of Korea [2013K1A1A2A02078326] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.
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