Next Article in Journal
Secure and Cost-Effective Distributed Aggregation for Mobile Sensor Networks
Previous Article in Journal
Localized Electrical Impedance Myography of the Biceps Brachii Muscle during Different Levels of Isometric Contraction and Fatigue
Article Menu

Export Article

Open AccessArticle
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
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)
View Full-Text   |   Download PDF [3734 KB, uploaded 22 April 2016]   |  


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

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top