Paper Info
Title
Feature Learning For Fingerprint-Based Positioning In Indoor Environment
Abstract
Recent years have witnessed a growing interest in using Wi-Fi received signal strength for indoor fingerprint-based positioning. However, previous study about this problem has primarily faced two main challenges. One is that positioning fingerprint feature using received signal strength is unstable due to heterogeneous devices and dynamic environment status, which will greatly degrade the positioning accuracy. Another is that some improved positioning fingerprint features will suffer the curse of dimensionality in online positioning. In this paper, we designed a novel positioning fingerprint feature using the segment similarity of Wi-Fi access points, considering both the received signal strength value and the Wi-Fi access point. Based on this designed fingerprint feature, we proposed a two-stage positioning algorithm for indoor fingerprint-based positioning. Experiment results indicate that our proposed positioning methodology can not only achieve better positioning performance but also consume less positioning time compared to three baseline methods.
Year
DOI
Venue
2015
10.1155/2015/452590
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Field
DocType
Volume
Hybrid positioning system,Computer vision,Computer science,Fingerprint,Curse of dimensionality,Artificial intelligence,Signal strength,Feature learning
Journal
11
ISSN
Citations 
PageRank 
1550-1477
7
0.52
References 
Authors
33
5
Name
Order
Citations
PageRank
Zengwei Zheng1277.99
yuanyi chen2171.10
tao he3171.10
Lin Sun41459.46
dan chen5254.24