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...

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Bibliographic Details
Published inIEEE transactions on intelligent transportation systems Vol. 23; no. 12; pp. 25047 - 25053
Main Authors Min, Dongchan, Kim, Minchan, Lee, Jinsil, Circiu, Mihaela Simona, Meurer, Michael, Lee, Jiyun
Format Journal Article
LanguageEnglish
Published New York IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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
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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