Table of Contents
Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 240564, 14 pages
Research Article

A Unified Framework for GPS Code and Carrier-Phase Multipath Mitigation Using Support Vector Regression

1University of Information Technology, Km 20, Ha Noi Highway, Linh Trung Ward, Thu Duc, HCMC 70000, Vietnam
2Singapore Institute of Technology, 25 North Bridge Road, Singapore 179104
3School of Information Science and Technology, University of Science and Technology of China, No. 443 Huangshan Road, Hefei, Anhui 230027, China

Received 1 October 2012; Accepted 14 January 2013

Academic Editor: Paolo Gastaldo

Copyright © 2013 Quoc-Huy Phan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multipath mitigation is a long-standing problem in global positioning system (GPS) research and is essential for improving the accuracy and precision of positioning solutions. In this work, we consider multipath error estimation as a regression problem and propose a unified framework for both code and carrier-phase multipath mitigation for ground fixed GPS stations. We use the kernel support vector machine to predict multipath errors, since it is known to potentially offer better-performance traditional models, such as neural networks. The predicted multipath error is then used to correct GPS measurements. We empirically show that the proposed method can reduce the code multipath error standard deviation up to 79% on average, which significantly outperforms other approaches in the literature. A comparative analysis of reduction of double-differential carrier-phase multipath error reveals that a 57% reduction is also achieved. Furthermore, by simulation, we also show that this method is robust to coexisting signals of phenomena (e.g., seismic signals) we wish to preserve.