Relative entropy-based Kalman filter for seamless indoor/outdoor multi-source fusion positioning with INS/TC-OFDM/GNSS

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 technolo...

Full description

Saved in:
Bibliographic Details
Published in:Cluster computing 2019-07, Vol.22 (Suppl 4), p.8351-8361
Main Authors: Hu, Enwen, Deng, Zhongliang, Xu, Qingqing, Yin, Lu, Liu, Wen
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Coupling
Data sources
Entropy
Global navigation satellite system
Global positioning systems
GPS
Inertial navigation
Kalman filters
Museums
Navigation systems
Operating Systems
Orthogonal Frequency Division Multiplexing
Outliers (statistics)
Processor Architectures
Reliability
Velocity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-1803-1