Neural networks based approach for fine tracking in satellite navigation systems

Abstract

Abstract-In this paper a novel method to solve the fine synchronization problem in GNSS receivers is presented. In fact the extended evolution of GNSS-based applications will imply the growth of fast and precise navigation systems. The aim of this study is to found an alternative solution to the classical non-coherent Delay Lock Loop. In particular, the proposed method, based on Self Organizing Map (SOM) a particular type of Neural Networks, allows to improve the performances in multipath channel.

A novel approach to spatial data fusion based on Bayesian network

“…An algorithm based on the Dempster-Shafer evidence theory has been applied on simulated data and actual data (SPOT/HRV image and NOAA/AVHRR series), and the results show the performance of the proposed method [12]. A novel method based on neural networks to solve the fine synchronization problem in GNSS receivers is presented by Musso [13]. Zhao et al have proposed a multi-source ITS data fusion technique using Support Vector Machine (SVM), and designs a corresponding implementation approach from perspectives of SVM training, training result evaluation and SVM test [14].…”