DNN-Based Approach to Mitigate Multipath Errors of Differential GNSS Reference Stations

One of the major error components of differential global navigation satellite systems is a multipath error in a reference station. This paper introduces a deep neural network based multipath modeling method. A signal to noise ratio, as well as satellite geometry, is used as a feature parameter to ca...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-12, Vol.23 (12), p.25047-25053
Main Authors: Min, Dongchan, Kim, Minchan, Lee, Jinsil, Circiu, Mihaela Simona, Meurer, Michael, Lee, Jiyun
Format: Article
Language:English
Subjects:
Artificial neural networks
deep neural network
Differential global navigation satellite system
Errors
Geometry
Global navigation satellite system
Measurement uncertainty
multipath error mitigation
Navigation satellites
Receiving antennas
Satellite antennas
Satellites
Signal to noise ratio
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:One of the major error components of differential global navigation satellite systems is a multipath error in a reference station. This paper introduces a deep neural network based multipath modeling method. A signal to noise ratio, as well as satellite geometry, is used as a feature parameter to capture the variation of the multipath error caused by unavoidable changes in the vicinity of the reference station. The performance of the proposed method is demonstrated for both normal and varying multipath cases using experimental data. The remaining multipath error after mitigation is well bounded by the standardized error model.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3207281