Robust design of a machine learning-based GNSS NLOS detector with multi-frequency features

ABSTRACT: The robust detection of GNSS non-line-of-sight (NLOS) signals is of vital importance for land- and close-to-land-based safe navigation applications. The usage of GNSS measurements affected by NLOS can lead to large unbounded positioning errors and loss of safety. Due to the complex signal conditions in urban environments, the use of machine learning or artificial intelligence techniques and algorithms has recently been identified as potential tools to classify GNSS LOS/NLOS signals. The design of machine learning algorithms with GNSS features is an emerging field of research that must, however, be tackled carefully to avoid biased estimation results and to guarantee algorithms that can be generalized for different scenarios, receivers, antennas, and their specific installations and configu

SUBMITTER: Garcia Crespillo O 

PROVIDER: S-EPMC10416230 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature