Height measurement method based on feature enhancement multi-base integrated re-tracking model

Through the feature-enhanced multi-base integrated re-tracking model (FMERM), PCA and XGBoost models are used to optimize the re-tracking of the reflected signal waveform, which solves the problem of insufficient GNSS-R height measurement accuracy and achieves high-precision sea surface height measurement.

CN120802312APending Publication Date: 2025-10-17HARBIN INST OF TECH AT WEIHAI
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Patent Information

Application Number
CN202510628798.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-15
Publication Date
2025-10-17

AI Technical Summary

Technical Problem

Existing GNSS-R altimetry technology has problems such as inaccurate reflection waveform re-tracking and low precision. In particular, there is a lack of effective methods for ionospheric correction and receiver orbit error correction, resulting in insufficient sea surface height measurement accuracy.

Method used

The feature-enhanced multi-base integrated re-tracking model (FMERM) is adopted to extract features through principal component analysis (PCA) and merge them with the original features. The extended gradient boosting (XGBoost) model is combined for ensemble learning to optimize the re-tracking point positions of the reflected signal waveform, and the sea surface height is inverted using geometric relationships and atmospheric correction.

Benefits of technology

The precision and accuracy of GNSS-R sea surface height measurements have been improved. Through feature enhancement and ensemble learning methods, the accuracy of reflection waveform re-tracking has been significantly improved, providing high-precision sea surface height data support.

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Abstract

The invention relates to the technical field of satellite altimetry, in particular to a height measurement method based on a feature-enhanced multi-base integrated re-tracking model, which is characterized in that the feature-enhanced multi-base integrated re-tracking model FMERM is constructed to optimize the position of a reflection waveform re-tracking point, and the FMERM model executes the following processing processes: 1, extracting input variable features by using PCA, and extracting the input variable features by using PCA; combining with an original input variable to form an enhanced feature set; secondly, optimizing the hyper-parameters of the XGBoost model by using a grid search method, so that the hyper-parameters of the XGBoost model are optimal; thirdly, utilizing the trained model to accurately calculate a re-tracking point normalization power value of the reflected signal waveform; fourthly, the sea surface height is inverted through the geometrical relationship and direct reflection signal time delay and atmospheric correction; an effective means is provided for solving the problem that a reflection waveform re-tracking method is inaccurate, and powerful support is provided for high-precision satellite-borne GNSS-R sea surface height measurement.
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Citation Information

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