Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

  1. Home
  3. Research Domains
  5. Engineering
  7. Communications Engineering
  9. Data Communications
  11. Transxlt: A Novel Ztd Prediction Method With Sasr-based Data Reconstruction.
  1. Home
  3. Research Domains
  5. Engineering
  7. Communications Engineering
  9. Data Communications
  11. Transxlt: A Novel Ztd Prediction Method With Sasr-based Data Reconstruction.

Related Experiment Video

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599

TransXLT: A novel ZTD prediction method with SASR-based data reconstruction.

Shicheng Xie1,2, Xuexiang Yu1,2, Jiajia Yuan2,3

  • 1School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China.

Iscience
|April 25, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new model, transformer-xLSTM (TransXLT), improves Zenith Tropospheric Delay (ZTD) prediction accuracy by integrating Global Navigation Satellite System (GNSS) data, ERA5, and GPT3. It effectively handles data loss using sparse attention-based time series reconstruction (SASR).

Keywords:
Applied sciencesComputer scienceNavigation System

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K

Related Experiment Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.1K

Area of Science:

  • Geodesy and Satellite Navigation
  • Atmospheric Science and Meteorology
  • Artificial Intelligence and Machine Learning

Background:

  • Traditional Zenith Tropospheric Delay (ZTD) models struggle with accuracy during complex weather and data gaps.
  • Global Navigation Satellite System (GNSS) data are crucial for precise positioning but are affected by tropospheric delays.

Purpose of the Study:

  • To develop an advanced model for accurate ZTD estimation, addressing limitations of existing methods.
  • To enhance ZTD prediction accuracy by integrating diverse data sources and robust data imputation techniques.

Main Methods:

  • A novel transformer-xLSTM (TransXLT) model was developed, combining spatial-temporal information from GNSS, ERA5, and GPT3.
  • A sparse attention-based time series reconstruction (SASR) method was employed to handle missing GNSS data.

Main Results:

  • SASR reduced Mean Absolute Error (MAE) by 24.5% and training Root Mean Square Error (RMSE) by 15.1% under significant data loss.
  • The TransXLT model achieved an average RMSE of 8.13 mm across six sites, outperforming benchmarks by up to 76.54%.
  • The model demonstrated robustness across different latitudes, altitudes, and seasons.

Conclusions:

  • The TransXLT model significantly improves ZTD estimation accuracy for GNSS applications, especially under challenging conditions.
  • The integration of advanced AI techniques and multi-source data offers a promising direction for precise atmospheric parameter estimation.