Fusion of PSO-SVM and ICEEMDAN for high stability GNSS-MR sea level height estimation-Reference-Cited by-同舟云学术

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Fusion of PSO-SVM and ICEEMDAN for high stability GNSS-MR sea level height estimation

Published:2024-03-22 Issue:1 Volume:36 Page:1-15
ISSN:2766-9645
Container-title:All Earth
language:en
Short-container-title:All Earth

Author:

Jian Linghuo¹,Wang Xinpeng¹²³,Hao Haining¹,Wang Hong¹,Yang Longshan¹

Affiliation:

1. Department of Surveying and Mapping Engineering, College of Mining, Guizhou University, Guiyang, China

2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, Wuhan, China

3. GNSS Research Center, Wuhan University, Wuhan, China

Funder

Key Laboratory of Geo-space Environment and Geodesy, Ministry of Education, Wuhan University

Introduced Talents Research Fund Project of Guizhou University

Cultivation Project of Guizhou University, China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/27669645.2024.2331328

Reference52 articles.

1. GNSS-R open-loop difference phase altimetry: Results from a bridge experiment

2. Combining ERA5 data and CYGNSS observations for the joint retrieval of global significant wave height of ocean swell and wind wave: a deep convolutional neural network approach

3. COMPARISON OF TWO SEPARATION MULTIPATH TECHNIQUES IN GNSS REFLECTOMETRY FOR SEA LEVEL DETERMINATION IN INDONESIA

4. Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation

5. Spaceborne GNSS-R Minimum Variance Wind Speed Estimator

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1. Improved tide level prediction model combined GA-BP neural networks and GNSS SNR data;Advances in Space Research;2024-08

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