Paper Info
Title
Fingerprinting-based Indoor Localization with Relation Learning Network
Abstract
Recently, fingerprinting-based indoor localization in deep learning framework has attracted intensive interests with the satisfactory accuracy. However, the location performance counts on the sufficient and massive radio signal acquisition, which is impractical to realize real-time localization for a wide range of location based services. To address this issue, we develop ReFi location system that can localize the target by relation learning network with a small dataset. ReFi firstly learns to represent the appropriate features and then compare them with the similar relation from the training and testing location data. According to relation learning, the target location is associated with the training locations and estimated in a probabilistic method. The extensive experimental results demonstrate that the proposed location system can achieve decimeter level accuracy with a single transceiver pair.
Year
DOI
Venue
2019
10.1109/ICCChina.2019.8855882
2019 IEEE/CIC International Conference on Communications in China (ICCC)
Keywords
Field
DocType
Indoor localization,fingerprinting,relation network,channel state information
Transceiver,Computer science,Location-based service,Real-time computing,Probabilistic method,Location data,Artificial intelligence,Deep learning,Location systems,Learning network,Channel state information
Conference
ISSN
ISBN
Citations 
2377-8644
978-1-7281-0733-2
0
PageRank 
References 
Authors
0.34
7
2
Name
Order
Citations
PageRank
Lingyan Zhang1104.38
Hongyu Wang258846.69