MPCNet: GNSS Multipath Error Compensation Network via Multi-task learning

In a multipath channel environment, classifying non-line-of-sight (NLOS) Global Navigation Satellite (GNSS) satellites and compensating multipath ranging error (MRE) is the most important task for improving GNSS positioning accuracy in urban areas. Recently, Signal-to-noise ratio (SNR), pseudorange, and other measurements have been used to classify NLOS satellites, but these measurements have limited representation of NLOS channel characteristics. In this paper, we propose a Multipath error Compensation Network (MPCNet) that uses an Autocorrelation function (ACF) output and 3D Geographic Information System (GIS) as inputs to classify NLOS satellites and compensate for MRE. MPCNet is composed of two heads for each task and a shared network that learns relevant information about the multipath channel environment from the ACF output. The performance evaluation of MPCNet was performed in a real urban environment, and the NLOS classification accuracy was compared with that of conventional deep learning-based NLOS classifiers, and the positioning performance of conventional positioning methods was also compared. MPCNet showed an NLOS classification performance of about 97% and an improvement in positioning accuracy of about 57% compared to conventional positioning methods, demonstrating that it is a robust and accurate MRE compensation technique in multipath environment.