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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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2022-12, Vol.23 (12), p.25047-25053 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Min, Dongchan Kim, Minchan Lee, Jinsil Circiu, Mihaela Simona Meurer, Michael Lee, Jiyun |
| description | 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. |
| doi_str_mv | 10.1109/TITS.2022.3207281 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| 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 |
| title | DNN-Based Approach to Mitigate Multipath Errors of Differential GNSS Reference Stations |
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