Search

Your search keyword '"Hak-beom Lee"' showing total 3 results
Start Over Author "Hak-beom Lee"
3 results on '"Hak-beom Lee"'

1. Feasibility Analysis of GPS L2C Signals for SSV Receivers on SBAS GEO Satellites

Author

Hak-Beom Lee, Ki-Ho Kwon, and Jong-Hoon Won

Subjects
Global Positioning System (GPS) L2C, space service volume (SSV), satellite-based augmentation system (SBAS), geostationary orbit (GEO), Science
Abstract

This paper analyzes the feasibility of Global Positioning System (GPS) L2C signals for use with the space service volume (SSV) receiver on satellite-based augmentation system (SBAS) geostationary orbit (GEO) satellites equipped with L1 and L5 band signal transmitters. Augmentation signals transmitted at L1 and L5 bands from SBAS GEO satellites may interfere with the same bands of SSV GPS-receiving antennas. Therefore, the use of L1 and L5 band signals for the GPS SSV receiver on SBAS GEO satellites is prohibited, and the GPS L2C signal is selected. Unlike ground systems, the various constraints of space exploration in GEO should be considered. Therefore, signal feasibility analysis is essential before considering the use of new global navigation satellite system (GNSS) signals in GEO. This paper presents satellite visibility, dilution of precision, and navigation solution error when the GPS L2C signal is used in GEO satellites through numerical simulation.

Published
2022
Full Text
View/download PDF

2. Precise Positioning with Machine Learning based Kalman Filter using GNSS/IMU Measurements from Android Smartphone

Author

Jong-Hoon Won, Young-Jin Song, Hak-beom Lee, Kahee Han, Dong-Hyuk Park, and Subin Lee

Subjects
Noise, Inertial measurement unit, business.industry, GNSS applications, Robustness (computer science), Computer science, Covariance matrix, Filter (video), Computer vision, Kalman filter, Artificial intelligence, Covariance, business
Abstract

This paper presents GNSS/INS integration Kalman filter for enhancement of positioning accuracy and robustness to surrounding environment. In the Kalman filter system, filter parameters such as process noise covariance and measurement noise covariance selected in the tuning process determine the characteristics of the overall system. Therefore, the empirical knowledge of the filter designer should be fully employed in the tuning process, and finding proper parameter values is still a challenging work. We adopt reinforcement learning to find the process noise covariance of the filter parameter. The experimental results show that the improvement of navigation performance is achieved by the efficient use of the learned process noise covariance matrix.

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results