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Machine learning based LOS/NLOS classifier and robust estimator for GNSS shadow matching

Satellite Navigation 2020年第1期1卷 167-178页

作者： Haosheng Xu antonio angrisano Salvatore Gaglione Li-Ta Hsu The Hong Kong Polytechnic University KowloonHong KongPeople’s Republic of China The University of Benevento G.Fortunato BeneventoItaly.3 The University of Naples ParthenopeNaplesItaly. The University of Naples Parthenope NaplesItaly

Global Navigation Satellites Systems(GNSS)is frequently used for positioning services in various applications,e.g.,pedestrian and vehicular ***,it is well-known that GNSS positioning performs unreliably in urban *** shadow matching is a method of improving accuracy in the cross-street *** position and classification of observed satellite visibility between line-of-sight(LOS)and non-line-of-sight(NLOS)are essential for its *** the conventional LOS/NLOS classification,the classifiers are based on a single feature,extracted from raw GNSS measurements,such as signal noise ratio,pseudorange,elevation angle,*** in urban canyons,these measurements are unstable and unreliable due to the signal reflection and refraction from the surrounding ***,the conventional least square approach for positioning is insufficient to provide accurate initialization for shadow matching in urban *** our study,shadow matching is improved using the initial position from robust estimator and the satellite visibility determined by support vector machine(SVM).The robust estimator has an improved positioning accuracy and the classification rate of SVM classification can reach 91.5%in urban *** important issue is related to satellites with ultra-high or low elevation angles and satellites near the building boundary that are very likely to be *** solving this problem,the SVM classification shows the potential of about 90%classification accuracy for various urban *** the help of these approaches,the shadow matching has a mean error of 10.27 m with 1.44 m in the cross-street direction;these performances are suitable for urban positioning.

关键词： GNSS Shadow matching NLOS and machine learning Robust estimation

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