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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

Authors

Yiming Quan

Lawrence Lau

Gethin Wyn Roberts

Xiaolin Meng

Chao Zhang



Abstract

© 2018 by the authors. 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.

Citation

Quan, Y., Lau, L., Roberts, G. W., Meng, X., & Zhang, C. (2018). Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning. Remote Sensing, 10(12), Article 2052. https://doi.org/10.3390/rs10122052

Journal Article Type Article
Acceptance Date Dec 14, 2018
Online Publication Date Dec 17, 2018
Publication Date Dec 17, 2018
Deposit Date Jan 8, 2019
Publicly Available Date Jan 8, 2019
Journal Remote Sensing
Electronic ISSN 2072-4292
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 10
Issue 12
Article Number 2052
DOI https://doi.org/10.3390/rs10122052
Keywords General Earth and Planetary Sciences
Public URL https://nottingham-repository.worktribe.com/output/1449740
Publisher URL https://www.mdpi.com/2072-4292/10/12/2052

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