Convolutional neural network based multipath detection method for static and kinematic GPS high precision positioning

Quan, Yiming and Lau, Lawrence and Roberts, Gethin Wyn and 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

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Abstract

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.

Item Type: Article
Keywords: Global Positioning System (GPS); Convolutional Neural Network (CNN); multipath detection; machine learning; high precision positioning
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Civil Engineering
University of Nottingham, UK > Faculty of Engineering > Department of Civil Engineering
Identification Number: https://doi.org/10.3390/rs10122052
Depositing User: Zhou, Elsie
Date Deposited: 08 Jan 2019 09:06
Last Modified: 08 Jan 2019 09:06
URI: http://eprints.nottingham.ac.uk/id/eprint/55854

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