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Sensors 2016, 16(4), 580; doi:10.3390/s16040580

Outlier Detection in GNSS Pseudo-Range/Doppler Measurements for Robust Localization

SATIE (Systems Applications of Information Energy Technologies) laboratory, University of Paris-Sud, 91405 Orsay, France
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Author to whom correspondence should be addressed.
Academic Editor: Davide Brunelli
Received: 24 January 2016 / Revised: 18 April 2016 / Accepted: 19 April 2016 / Published: 22 April 2016
(This article belongs to the Section Physical Sensors)
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Abstract

In urban areas or space-constrained environments with obstacles, vehicle localization using Global Navigation Satellite System (GNSS) data is hindered by Non-Line Of Sight (NLOS) and multipath receptions. These phenomena induce faulty data that disrupt the precise localization of the GNSS receiver. In this study, we detect the outliers among the observations, Pseudo-Range (PR) and/or Doppler measurements, and we evaluate how discarding them improves the localization. We specify a contrario modeling for GNSS raw data to derive an algorithm that partitions the dataset between inliers and outliers. Then, only the inlier data are considered in the localization process performed either through a classical Particle Filter (PF) or a Rao-Blackwellization (RB) approach. Both localization algorithms exclusively use GNSS data, but they differ by the way Doppler measurements are processed. An experiment has been performed with a GPS receiver aboard a vehicle. Results show that the proposed algorithms are able to detect the ‘outliers’ in the raw data while being robust to non-Gaussian noise and to intermittent satellite blockage. We compare the performance results achieved either estimating only PR outliers or estimating both PR and Doppler outliers. The best localization is achieved using the RB approach coupled with PR-Doppler outlier estimation. View Full-Text
Keywords: Global Navigation Satellite Systems (GNSS); robust localization; a contrario decision; particle filter; Rao-Blackwellization Global Navigation Satellite Systems (GNSS); robust localization; a contrario decision; particle filter; Rao-Blackwellization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zair, S.; Le Hégarat-Mascle, S.; Seignez, E. Outlier Detection in GNSS Pseudo-Range/Doppler Measurements for Robust Localization. Sensors 2016, 16, 580.

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