Convolutional Neural Network Based Multipath Detection Method for Static and Kinematic GPS High Precision Positioning

Autor:	Quan, Yiming, Lau, Lawrence, Roberts, Gethin Wyn, Meng, Xiaolin, Zhang, Chao
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	machine learning high precision positioning Science ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Computer Science::Networking and Internet Architecture multipath detection Global Positioning System (GPS) Convolutional Neural Network (CNN) Computer Science::Information Theory
Zdroj:	Remote Sensing, Vol 10, Iss 12, p 2052 (2018)
ISSN:	2072-4292
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1ef5342af34f5863cc009ae14ba3e364 https://www.mdpi.com/2072-4292/10/12/2052 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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