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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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 |
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DRNTU::Engineering::Computer science and engineering Tan, Su-Lim Phan, Quoc-Huy McLoughlin, Ian Vince GPS multipath mitigation : a nonlinear regression approach |
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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. |
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School of Computer Engineering |
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School of Computer Engineering Tan, Su-Lim Phan, Quoc-Huy McLoughlin, Ian Vince |
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Article |
author |
Tan, Su-Lim Phan, Quoc-Huy McLoughlin, Ian Vince |
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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 |
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GPS multipath mitigation : a nonlinear regression approach |
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GPS multipath mitigation : a nonlinear regression approach |
title_sort |
gps multipath mitigation : a nonlinear regression approach |
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2013 |
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https://hdl.handle.net/10356/99949 http://hdl.handle.net/10220/16267 |
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1681058959099691008 |