Abstract
Since the middle ages, the need to identify the vehicles position in their local environment has always been a necessity and a challenge. Today, GNSS-based positioning systems have penetrated several field, such as land transport, emergency systems and civil aviation requiring high positioning accuracy. However, the performances of GNSS-based systems can be degraded in harsh environment due to non-line-of-sight (NLOS), Multipath and masking effects. In this paper, for improving vehicle localization in urban canyons, we address a very challenging problem related to GNSS signal reception state detection (LOS, NLOS or Multipath). A SVMbased system for GNSS Multipath detection using the fusion of information provided by two GNSS antennas is proposed. In this work, we aim to explore the potential of machine learning, and more precisely, Support Vector Machines (SVM) to identify GNSS signals reception state. The SVM-based system developed in this work has used the C/N0 of signals provided by RHCP and LHCP antennas, and satellite elevation as classification criteria. The training data set is constructed by several experimental studies done in real environments, Calais, France . Furthermore, four SVM kernel functions are tested, namely, Linear, Gaussian, Cubic and Quadratic. A GNSS signal reception state detection by applying the proposed SVM-based classifier is demonstrated on real GPS signals, and the efficiency of the system is shown. We obtain empirically an accuracy of signal detection about 93%.
Highlights
In harsh environment, the performances of GNSS-based positioning systems can be strongly affected, due to Multipath effects
We have exploited the potential of the right-hand circular polarized (RHCP) and left-hand circular polarized (LHCP) to process the GNSS signal. we have proposed a new classifier of GNSS signals, based on a very robust technique of Machine learning called Support Vector Machines (SVM) [18][19]
We propose a new GNSS LOS/Multipath signal classifier based on the SVM and the fusion of information provided by the RHCP and LHCP antennas
Summary
The performances of GNSS-based positioning systems can be strongly affected, due to Multipath effects. Other investigations have shown that fuzzy logic approach can be applied to classify GPS data based on signal degradation conditions [10,11,12,13]. In [10], a GPS-code-based measurements fuzzy processing is applied to detect the optimal observables, using the GDOP (Geometrical Dilution Of Precision) parameter and the signal-to-noise ratio (SNR). Others works have applied fuzzy logic to detect the signal quality using C/N0 and the dilution-of-precision (DOP) [12,13,14]. Other approach is proposed by Monask Socharoentum in [17], it’s based on Nav2Nav (Navigation-to-Navigation) technique in which pseudo-range corrections and machine learning work together to detect GNSS Multipath signals. A signal reception state detection by applying the proposed classifier system is demonstrated on real GPS signals, and the efficiency of the classifier is shown
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