LOS/Multipath/NLOS Classifiers using Machine learning and Raytracing. A preliminary study to identify and address the Mulitpath error
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2024
Författare
Upendra, Karthik
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis explores the application of machine learning algorithms to address one
of the challenges in localization in GNSS called the Multipath errors. The approach
involves data collection via a receiver mounted on an excavator which is placed in
front of the building, which acts as one of the sources for mulitpath error. In order
to perform supervised machine learning, wireless communication tool box within
Matlab is used for Raytracing simulation to label the data.
Drawing inspiration from existing literature, we introduce a novel feature, the ’differ ence in range acceleration between code and carrier signals,’ which exhibits promis ing distribution and metrics when analyzed with respect to ray-tracing results.
The support vector machine (SVM) achieves an average class-wise recall and preci sion of approximately 75% on the recorded measurments. Additionally, we explore
the use of an Autoencoder with a 1D-CNN layer to extract new features aimed
at enhancing classification performance. By conducting three different simulations
with varying data sorting methods, we demonstrate how sorting the data and the
machine learning algorithm can influence the learned features which in turn impacts
classification performance.
Lastly, in order to take advantage of the insights gained from different sorting meth ods, we transform the problem from a Multiclass to Multi-label-Multi-output clas sification problem, wherein we utilize a deep neural network architecture with GRU
units to classify the signals. Although complicated in terms of data restructuring
and handling, the network demonstrated robust performance, achieving more than
85% average class-wise recall and precision and exceeding 96% for signals labeled
LOS.
Beskrivning
Ämne/nyckelord
Machine learning, Multipath error, GNSS, Convolutional neural network (CNN), Recurrent neural network (RNN), Gated recurrent unit (GRU), 1D CNN, Autoencoders, Raytracing, Deep neural network (DNN).