Paper Info
Title
Wimage: Crowd Sensing Based Heterogeneous Information Fusion For Indoor Localization
Abstract
Crowd sensing is an efficient way to collect heterogeneous information in the complicated infrastructures for fingerprinting based indoor localization. However, the information related to the dynamic trajectory are difficult to fuse due to the reliability issues from different devices and user moving habits. In this paper, we proposed a crowd sensing based indoor localization system with heterogeneous information fusion, which is called Wimage. Wimage can efficiently fuse multiple information sources related to location information, e.g., visual image, WiFi and geomagnetic data, even if the targets are moving with different and variable speeds. Then we design image-base subregion matching algorithm to locate the initial position and segmented weighted K-nearest neighbor algorithm to attain the matched trajectories in the database. A dynamic temporal warping algorithm is proposed for further calibrating the estimations. The experimental results indicate that with the helps from different kinds of information, the root mean square error is only below 0.4m, which is highly accurate for locating a target in a large scale of indoor environment.
Year
DOI
Venue
2020
10.1109/WCNC45663.2020.9120796
2020 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
Keywords
DocType
ISSN
Indoor positioning, heterogeneous information fusion, WiFi fingerprinting, visual image matching, geomagnetic calibration
Conference
1525-3511
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Fangmin Li100.34
Yubin Zhao26914.59
xiaofan li37912.44
Z. Chen43443271.62