Method for improving height measurement precision based on machine learning weighted average fusion feature extraction

A technology of fused features and weighted average, applied in cross-fields, can solve problems such as the difficulty of obtaining high-precision sea surface heights, and achieve good inversion effects, high precision, and simple model algorithms

Pending Publication Date: 2022-03-04
CHINA ACADEMY OF SPACE TECHNOLOGY
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, machine learning models require a large number of labeled observations to train and build models
Spaceborne GNSS-

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for improving height measurement precision based on machine learning weighted average fusion feature extraction
  • Method for improving height measurement precision based on machine learning weighted average fusion feature extraction
  • Method for improving height measurement precision based on machine learning weighted average fusion feature extraction

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0059] In order to make the object, technical solution and advantages of the present invention clearer, the embodiments disclosed in the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0060] One of the core ideas of the present invention is to introduce the fusion model in machine learning to assist GNSS-R to perform delay re-tracking and sea surface height inversion, and to improve the measurement accuracy by increasing the available information of DDM. The inversion essence based on machine learning sea surface height is the non-linear regression problem of supervised learning, the present invention has analyzed single regression model (such as, linear regression model, ElasticNet regression model and support vector machine SVR regression model etc.) and ensemble tree regression at first High inversion accuracy of commonly used regression models in machine learning such as GBDT regression model, XGBoost regression ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for improving height measurement precision based on machine learning weighted average fusion feature extraction, which utilizes a global navigation satellite system reflectometer to perform sea surface height measurement and can improve observation and inversion of a mesoscale process of an ocean by increasing space coverage of ocean surface observation. In order to make up the defects of a traditional inversion method, a novel machine learning weighted average fusion feature extraction method with airborne time delay waveform data as input and sea surface height as output is constructed on the basis of a machine learning fusion model in combination with a feature extraction principle. Meanwhile, two features of HALF and DER which are sensitive to sea surface height change are constructed based on a time delay waveform data set, and the influence of feature sets of different information details on sea surface height inversion precision is analyzed. The inversion precision of the sea surface height can be effectively improved by adopting a novel machine learning weighted average fusion feature extraction method, and the precision is improved by about 61%.

Description

technical field [0001] The invention belongs to the interdisciplinary technical fields of satellite altimetry and marine surveying and mapping, and in particular relates to a method for improving measurement accuracy based on machine learning weighted average fusion feature extraction. Background technique [0002] As an important ocean parameter, sea surface height plays an important role in establishing global tidal models, observing large-scale ocean circulation, and monitoring global sea level changes. The traditional spaceborne radar altimeter obtains the information of ocean physical parameters by continuously transmitting radar pulses to the earth and receiving sea surface echoes, which has the disadvantages of low coverage, long repetition period, and high satellite cost. GNSS-R technology is a new remote sensing technology for sea surface surveying in recent years. It retrieves the sea surface height by measuring the time delay between the reflected signal and the d...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06N20/00
CPCG06N20/00G06F18/253
Inventor 郑伟吴凡王强
Owner CHINA ACADEMY OF SPACE TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap