|Abstract:||Recent trends in Global Navigation Satellite System (GNSS) applications in urban environments have led to a proliferation of studies in this field that seek to mitigate the adverse effect of non-line-of-sight (NLOS) phenomena. However, these methods reduce the availability of positioning in deep urban conditions. For such harsh urban settings, this paper proposes a methodology of constructive use of NLOS signals, instead of their elimination. We propose to compensate for the NLOS errors using a 3D GNSS simulator to predict the measurements bias and integrate them as observations in the estimation method. We investigate a novel GNSS positioning technique based on measurement similarity scoring of an array of position candidates. We improve this technique using an estimation of the uncertainty on the bias prediction by 3D modeling. Experiment results using real GNSS data in a deep urban environment confirm the theoretical sub-optimal efficiency of the proposed approach, despite it intensive computational load.|
Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
|Pages:||3039 - 3052|
|Cite this article:||
Kbayer, Nabil, Sahmoudi, Mohamed, Ortega, Héctor, Rouch, Cédric, "Approximate Maximum Likelihood Estimation Using a 3D GNSS Simulator for Positioning in MP/NLOS Conditions," Proceedings of the 30th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 3039-3052.
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