GPS multipath mitigation : a nonlinear regression approach

Under the assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression prob...

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Main Authors: Tan, Su-Lim, Phan, Quoc-Huy, McLoughlin, Ian Vince
Other Authors: School of Computer Engineering
Format: Article
Language:English
Published: 2013
Subjects:
Online Access:https://hdl.handle.net/10356/99949
http://hdl.handle.net/10220/16267
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-999492020-05-28T07:17:49Z GPS multipath mitigation : a nonlinear regression approach Tan, Su-Lim Phan, Quoc-Huy McLoughlin, Ian Vince School of Computer Engineering DRNTU::Engineering::Computer science and engineering Under the assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression problem. We present a method for estimating code multipath error of GPS ground fixed stations. By formulating the multipath estimation as a regression problem, we construct a nonlinear continuous model for estimating multipath error based on well-known sparse kernel regression, for example, support vector regression. We will empirically show that the proposed method achieves state-of-the-art performance on code multipath mitigation with 79 % reduction on average in terms of standard deviation of multipath error. Furthermore, by simulation, we will also show that the method is robust to other coexisting signals of phenomena, such as seismic signals. 2013-10-04T06:07:57Z 2019-12-06T20:13:56Z 2013-10-04T06:07:57Z 2019-12-06T20:13:56Z 2012 2012 Journal Article Phan, Q. H., Tan, S. L., & McLoughlin, I. (2012). GPS multipath mitigation : a nonlinear regression approach. GPS solutions, 17(3), 371-380. https://hdl.handle.net/10356/99949 http://hdl.handle.net/10220/16267 10.1007/s10291-012-0285-5 en GPS solutions
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Tan, Su-Lim
Phan, Quoc-Huy
McLoughlin, Ian Vince
GPS multipath mitigation : a nonlinear regression approach
description Under the assumption that the surrounding environment remains unchanged, multipath contamination of GPS measurements can be formulated as a function of the sidereal repeatable geometry of the satellite with respect to the fixed receiver. Hence, multipath error estimation amounts to a regression problem. We present a method for estimating code multipath error of GPS ground fixed stations. By formulating the multipath estimation as a regression problem, we construct a nonlinear continuous model for estimating multipath error based on well-known sparse kernel regression, for example, support vector regression. We will empirically show that the proposed method achieves state-of-the-art performance on code multipath mitigation with 79 % reduction on average in terms of standard deviation of multipath error. Furthermore, by simulation, we will also show that the method is robust to other coexisting signals of phenomena, such as seismic signals.
author2 School of Computer Engineering
author_facet School of Computer Engineering
Tan, Su-Lim
Phan, Quoc-Huy
McLoughlin, Ian Vince
format Article
author Tan, Su-Lim
Phan, Quoc-Huy
McLoughlin, Ian Vince
author_sort Tan, Su-Lim
title GPS multipath mitigation : a nonlinear regression approach
title_short GPS multipath mitigation : a nonlinear regression approach
title_full GPS multipath mitigation : a nonlinear regression approach
title_fullStr GPS multipath mitigation : a nonlinear regression approach
title_full_unstemmed GPS multipath mitigation : a nonlinear regression approach
title_sort gps multipath mitigation : a nonlinear regression approach
publishDate 2013
url https://hdl.handle.net/10356/99949
http://hdl.handle.net/10220/16267
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