Paper Info
Title
Relative entropy-based Kalman filter for seamless indoor/outdoor multi-source fusion positioning with INS/TC-OFDM/GNSS
Abstract
The current single data source positioning navigation systems cannot meet the high precision and high reliability required for indoor/outdoor positioning service. In this study, based on an inertial navigation system, time-and-code division-orthogonal frequency division multiplexing ranging technology and a global navigation satellite system, a relative entropy-based Kalman multi-source fusion positioning model is developed. First, multi-source numerical observation data are filtered, and the outliers are processed in data layers to improve data source reliability and to extract stable observation data. Next, the degree of the multi-source data coupling is quantified in an information layer to analyze the multi-source information coupling degree and to develop a coupling degree factor and a Kalman fusion positioning model for multi-source heterogeneous information. Tests show that this method significantly improves system positioning, navigation stability and positioning precision.
Year
DOI
Venue
2019
10.1007/s10586-018-1803-1
Cluster Computing
Keywords
DocType
Volume
TC-OFDM, INS, GNSS, Relative entropy, Indoor/outdoor seamless
Journal
22
Issue
ISSN
Citations 
Supplement
1573-7543
0
PageRank 
References 
Authors
0.34
13
5
Name
Order
Citations
PageRank
Enwen Hu111.02
Zhongliang Deng2217.47
Qingqing Xu300.34
Lu Yin423.53
Wen Liu544.15