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

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 12; p. 2052
• Main Authors: Quan, Yiming, Lau, Lawrence, Roberts, Gethin Wyn, Meng, Xiaolin, Zhang, Chao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2018
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Back propagation; Civil engineering; Computer simulation; Convolutional Neural Network (CNN); Distance learning; Global Positioning System (GPS); Global positioning systems; GPS; high precision positioning; Kinematics; Machine learning; Methods; Mitigation; multipath detection; Navigation; Neural networks; Noise; Principal components analysis; Receivers & amplifiers; Remote sensing; Satellite navigation systems; Satellites; Software; Sparsity; Stochastic models; Terrestrial environments; Time series; Voice recognition; United Kingdom > UK; China; Faroe Islands; Ningbo China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10122052

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.