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GNSS Positioning Enhancement Based on NLOS Multipath Biases Estimation Using Gaussian Mixture Noise
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Author(s): Guermah Bassma (STRS Laboratory, National Institute of Posts and Telecommunication, Morocco), Sadiki Tayeb (Faculty of Computing and Logistics, TICLab, International University of Rabat (UIR), Morocco)and El Ghazi Hassan (STRS Laboratory, National Institute of Posts and Telecommunication, Morocco)
Copyright: 2021
Pages: 21
Source title:
Research Anthology on Reliability and Safety in Aviation Systems, Spacecraft, and Air Transport
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5357-2.ch025
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Abstract
Global navigation satellite systems (GNSS) have been widely used in many applications where positioning plays an important role. However, the performances of these applications can be degraded in urban canyons, due to Non-Line-Of-Sight (NLOS) and Multipath interference affecting GNSS signals. In order to ensure high accuracy positioning, this article proposes to model the NLOS and Multipath biases by Gaussian Mixture noise using Expectation Maximization (EM) algorithm. In this context, an approach to estimate the Multipath and NLOS biases for real time positioning is presented and statistical tests for searching the probability distribution of NLOS and Multipath biases are illustrated. Furthermore, a hybrid approach based on PF (Particle Filter) and EM algorithm for estimating user position in hard environment is presented. Using real GPS (Global Positioning System) signal, the efficiency of the proposed approach is shown, and a significant improvement of the positioning accuracy over the simple PF estimation is obtained.
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