Skip to main content

GPS multipath mitigation: a nonlinear regression approach

Phan, Huy, Tan, Su-Lim, McLoughlin, Ian Vince (2013) GPS multipath mitigation: a nonlinear regression approach. GPS Solutions, 17 (3). pp. 371-380. ISSN 1080-5370. E-ISSN 1521-1886. (doi:10.1007/s10291-012-0285-5) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:48894)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/s10291-012-0285-5

Abstract

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.

Item Type: Article
DOI/Identification number: 10.1007/s10291-012-0285-5
Uncontrolled keywords: GPS, Multipath mitigation, Nonlinear regression, Support vector machine
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 25 Aug 2015 10:23 UTC
Last Modified: 16 Nov 2021 10:19 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48894 (The current URI for this page, for reference purposes)

University of Kent Author Information

  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.