Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning
Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on th...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2018
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/55854/ |
| _version_ | 1848799228142288896 |
|---|---|
| author | Quan, Yiming Lau, Lawrence Roberts, Gethin Wyn Meng, Xiaolin Zhang, Chao |
| author_facet | Quan, Yiming Lau, Lawrence Roberts, Gethin Wyn Meng, Xiaolin Zhang, Chao |
| author_sort | Quan, Yiming |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm. |
| first_indexed | 2025-11-14T20:32:20Z |
| format | Article |
| id | nottingham-55854 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:32:20Z |
| publishDate | 2018 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-558542019-01-08T09:06:25Z https://eprints.nottingham.ac.uk/55854/ Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning Quan, Yiming Lau, Lawrence Roberts, Gethin Wyn Meng, Xiaolin Zhang, Chao Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm. MDPI 2018-12-17 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/55854/1/remotesensing-10-02052-v3.pdf Quan, Yiming, Lau, Lawrence, Roberts, Gethin Wyn, Meng, Xiaolin and Zhang, Chao (2018) Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning. Remote Sensing, 10 (12). 2052/1-2052/18. ISSN 2072-4292 Global Positioning System (GPS); Convolutional Neural Network (CNN); multipath detection; machine learning; high precision positioning http://dx.doi.org/10.3390/rs10122052 doi:10.3390/rs10122052 doi:10.3390/rs10122052 |
| spellingShingle | Global Positioning System (GPS); Convolutional Neural Network (CNN); multipath detection; machine learning; high precision positioning Quan, Yiming Lau, Lawrence Roberts, Gethin Wyn Meng, Xiaolin Zhang, Chao Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning |
| title | Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning |
| title_full | Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning |
| title_fullStr | Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning |
| title_full_unstemmed | Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning |
| title_short | Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning |
| title_sort | convolutional neural network based multipath detection method for static and kinematic gps high precision positioning |
| topic | Global Positioning System (GPS); Convolutional Neural Network (CNN); multipath detection; machine learning; high precision positioning |
| url | https://eprints.nottingham.ac.uk/55854/ https://eprints.nottingham.ac.uk/55854/ https://eprints.nottingham.ac.uk/55854/ |