A Robust GNSS LOS/NLOS Signal Classifier

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91 Scopus citations


GNSS signal classification to LOS and NLOS signals is of great value for conventional ranging-based and shadow matching algorithms. The most common attribute for performing this classification is the signal strength. Alas, such classification is often insufficient, in particular, in urban environments. In this paper, we present a novel approach for LOS/NLOS classification utilizing supervised machine learning algorithms. Provided with a sufficiently large labeled training set, the proposed approach is able to predict with high certainty (>85 percent) the satellites' visibility status in dense urban regions. This achievement was possible due to the vast raw measurements supplied for the algorithm and using sophisticated feature-selection techniques.

Original languageEnglish
Pages (from-to)429-442
Number of pages14
JournalNavigation, Journal of the Institute of Navigation
Issue number4
StatePublished - 1 Dec 2016


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