TY - GEN
T1 - GNSS multipath detection using a machine learning approach
AU - Hsu, Li Ta
PY - 2018/3/14
Y1 - 2018/3/14
N2 - Insufficient localization accuracy of global navigation satellite system (GNSS) receivers is one of the challenges to implement advanced intelligent transportation system in highly urbanized areas. Multipath and non-line-of-sight (NLOS) effects strongly deteriorate GNSS positioning performance. This paper aims to train a classifier by supervised machine learning to separate the type of GNSS pseudorange measurement into three categories, clean, multipath and NLOS. Several features obtained or calculated from the GNSS raw data are evaluated. This paper also proposes a new feature to indicate the consistency between measurements of pseudorange and Doppler shift. According to the experiment result, about 75% of classification accuracy can be achieved using a support vector machine (SVM) classifier trained by the proposed feature and received signal strength.
AB - Insufficient localization accuracy of global navigation satellite system (GNSS) receivers is one of the challenges to implement advanced intelligent transportation system in highly urbanized areas. Multipath and non-line-of-sight (NLOS) effects strongly deteriorate GNSS positioning performance. This paper aims to train a classifier by supervised machine learning to separate the type of GNSS pseudorange measurement into three categories, clean, multipath and NLOS. Several features obtained or calculated from the GNSS raw data are evaluated. This paper also proposes a new feature to indicate the consistency between measurements of pseudorange and Doppler shift. According to the experiment result, about 75% of classification accuracy can be achieved using a support vector machine (SVM) classifier trained by the proposed feature and received signal strength.
KW - Global Positioning System
KW - Machine Learning
KW - Multipath
KW - NLOS
KW - Support Vector Machine
KW - Urban Area
UR - http://www.scopus.com/inward/record.url?scp=85046287873&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2017.8317700
DO - 10.1109/ITSC.2017.8317700
M3 - Conference article published in proceeding or book
AN - SCOPUS:85046287873
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1
EP - 6
BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Y2 - 16 October 2017 through 19 October 2017
ER -