International Conferences
| MPCNet: GNSS Multipath Error Compensation Network via Multi-Task Learning |
| Year |
2023 |
| Month |
|
| Journal |
2023 / IEEE Intelligent Vehicles Symposium (IV) |
| Author |
Sangjae Cho, Hong-Woo Seok, Seung-Hyun Kong* |
|
In a multipath channel environment, classifying non-line-of-sight (NLOS) Global Navigation Satellite (GNSS) satellites and estimating ranging errors due to multipath is the most important task for improving GNSS positioning accuracy in urban areas. Recently, Signal-to-noise ratio (SNR), pseudorange, elevation angle, and other measurements have been used to classify NLOS satellites, but these measurements have limited representation of NLOS channel characteristics and require receiver tracking and navigation stages, resulting in a decrease in computational efficiency. 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 a Convolutional Neural Network (CNN) to classify NLOS satellites and compensate for multipath pseudorange errors. MPCNet is composed of two heads for each task and a shared network that learns relevant information about the multipath channel environment from the input ACF. 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 positionings, such as Single Point Positioning (SPP) and Differential GPS (DGPS), was also compared. MPCNet showed an NLOS classification performance of about 97% and an improvement in positioning accuracy of about 57% compared to existing receivers, demonstrating that it is a robust and accurate multipath error compensation technique in urban environments. |
https://ieeexplore.ieee.org/document/10186566
28회 연결
