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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Bibliographic Details
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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Summary: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.