Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80399
Title: GPS signal reception classification using adaptive neuro-fuzzy inference system
Authors: Sun, R
Hsu, LT 
Xue, D
Zhang, G 
Ochieng, WY
Keywords: ANFIS
Multipath
NLOS
Urban Canyon
Issue Date: May-2019
Publisher: Cambridge University Press
Source: Journal of navigation, May 2019, v. 72, no. 3, p. 685-701 How to cite?
Journal: Journal of navigation 
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.
URI: http://hdl.handle.net/10397/80399
ISSN: 0373-4633
EISSN: 1469-7785
DOI: 10.1017/S0373463318000899
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