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GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

Published online by Cambridge University Press:  06 December 2018

Rui Sun Open the ORCID record for Rui Sun [Opens in a new window] ,
Li-Ta Hsu ,
Dabin Xue ,
Guohao Zhang  and
Washington Yotto Ochieng
Rui Sun
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China) (State Key Laboratory of Geo-Information Engineering, Xi'an Research Institute of Surveying and Mapping, Xi'an 710054, China)
Li-Ta Hsu*
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, the Hong Kong Polytechnic University, Hong Kong)
Dabin Xue
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)
Guohao Zhang
Affiliation:
(Interdisciplinary Division of Aeronautical and Aviation Engineering, the Hong Kong Polytechnic University, Hong Kong)
Washington Yotto Ochieng
Affiliation:
(College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China) (Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK)
*
(E-mail: lt.hsu@polyu.edu.hk)
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Abstract

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

Keywords

NLOSMultipathUrban CanyonANFIS
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 3 , May 2019 , pp. 685 - 701
Copyright
Copyright © The Royal Institute of Navigation 2018 

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