<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Positioning solutions need to be more precise and available. The most frequent method used nowadays includes a GPS receiver, sometimes supported by other sensors. Generally, GPS and GNSS suffer from spreading perturbations that produce biases on pseudo-range measurements. With a view to optimize the use of the satellites received, we offer a positioning algorithm with pseudo range error modelling with the contribution of an appropriate filtering process. Extended Kalman Filter, The Rao- Blackwellized filter are among the most widely used algorithms to predict errors and to filter the high frequency noise. This paper describes a new method of estimating the pseudo-range errors based on the PSO-RBF model which achieves an optimal training criterion. This model is appropriate of its method to predict the GPS corrections for accurate positioning, it reduce the positioning errors at high velocities by more than 50% compared to the RLS or EKF methods.</p></abstract>

Positioning solutions need to be more precise and available. The most frequent method used nowadays includes a GPS receiver, sometimes supported by other sensors. Generally, GPS and GNSS suffer from spreading perturbations that produce biases on pseudo-range measurements. With a view to optimize the use of the satellites received, we offer a positioning algorithm with pseudo range error modelling with the contribution of an appropriate filtering process. Extended Kalman Filter, The Rao- Blackwellized filter are among the most widely used algorithms to predict errors and to filter the high frequency noise. This paper describes a new method of estimating the pseudo-range errors based on the PSO-RBF model which achieves an optimal training criterion. This model is appropriate of its method to predict the GPS corrections for accurate positioning, it reduce the positioning errors at high velocities by more than 50% compared to the RLS or EKF methods.

GNSS Positioning Performance Analysis Using PSO-RBF Estimation Model

University Hassan II, Faculty of Sciences Casablanca, Morocco, Km 8 Eljadida road, P.B 5366,Maarif 20100 1 00212667948795,

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"GNSS Positioning Performance Analysis Using PSO-RBF Estimation Model"}

Positioning solutions need to be more precise and available. The most frequent method used nowadays includes a GPS receiver, sometimes supported by other...

GNSS Positioning Performance Analysis Using PSO-RBF Estimation Model

Publicado en línea: 26 abr 2017

Páginas: 146 - 154

DOI: https://doi.org/10.1515/ttj-2017-0014

Palabras clave
GNSS, pseudo-range, filtering, prediction, PSO-RBF

© Transport and Telecommunication Institute

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

GNSS Positioning Performance Analysis Using PSO-RBF Estimation Model

Publicado en línea: 26 abr 2017

Páginas: 146 - 154

DOI: https://doi.org/10.1515/ttj-2017-0014

Palabras claveGNSS, pseudo-range, filtering, prediction, PSO-RBF

© Transport and Telecommunication Institute

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Palabras clave
GNSS, pseudo-range, filtering, prediction, PSO-RBF