Retrieve the evaporation duct height by least-squares support vector machine algorithm

Douvenot, Rémi; Fabbro, Vincent; Bourlier, Christophe; Saillard, J.; Fuchs, H.-H.; Essen, H.; Foerster, Joerg

doi:10.1117/1.3081546

Cited by 13 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang [10] employed a particle swarm optimization (PSO) algorithm to invert a range direction inhomogeneous SBD M-profile. Moreover, a large number of researchers have used machine learning methods in the application of atmospheric ducts and achieved excellent inversion results [11][12][13][14][15][16]. However, both GA, PSO, SVM, and MLP algorithms require repeatedly using parabolic equations of the atmospheric ducts, which is timeconsuming.…”

Section: Introductionmentioning

confidence: 99%

Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion

Wei

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

A surface-based duct (SBD) is an abnormal atmospheric structure with a low probability of occurrence buta strong ability to trap electromagnetic waves. However, the existing research is based on the assumption that the range direction of the surface duct is homogeneous, which will lead to low productivity and large errors when applied in a real-marine environment. To alleviate these issues, we propose a framework for the inversion of inhomogeneous SBD M-profile based on a full-coupled convolutional Transformer (FCCT) deep learning network. We first designed a one-dimensional residual dilated causal convolution autoencoder to extract the feature representations from a high-dimension range direction inhomogeneous M-profile. Second, to improve efficiency and precision, we proposed a full-coupled convolutional Transformer (FCCT) that incorporated dilated causal convolutional layers to gain exponentially receptive field growth of the M-profile and help Transformer-like models improve the receptive field of each range direction inhomogeneous SBD M-profile information. We tested our proposed method performance on two sets of simulated sea clutter power data where the inversion of the simulated data reached 96.99% and 97.69%, which outperformed the existing baseline methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion

Wei

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Currently, machine learning has been widely used in many research fields, including artificial intelligence, financial industry, network security and so on, which has a significant impact on social productivity and economy. For atmospheric duct, machine learning prediction method is a new technology for EDH prediction with high efficiency and accuracy, which possesses great application value and achieved some results [33][34][35][36][37]. However, these researchers mainly use support vector machine (SVM) and feedforward neural network.…”

Section: Introductionmentioning

confidence: 99%

XGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm

Zhao¹,

Li²,

Zhao³

et al. 2020

RADIOENGINEERING

View full text Add to dashboard Cite

Evaporation duct is a specific atmospheric structure at sea, which has an important influence on the propagation path of electromagnetic waves (EW). Considering the limit of existing evaporation duct height (EDH) prediction models and aiming at proposing more accurate and stronger generalization ability of EDH models, we applied eXtreme Gradient Boosting (XGBoosting) algorithm to the field of evaporation duct for the first time. And we proposed the new EDH prediction model using XGBoost algorithm (XGB model). Simultaneously, traditional Paulus-Jeske (PJ) model and deep learning Multilayer Perceptron (MLP) model were introduced into the experiment to make a comparison. In terms of comprehensive performance, XGB model is optimal in all sub-regions and total area. Finally, cross-learning experiments were carried out to test the generalization ability of XGB model. The results show that the generalization ability of XGB model is better than that of MLP model.

show abstract

“…Machine learning is a kind of algorithm for data analysis, which can quickly mine the correlation and hidden laws in a large number of data and be used for regression prediction or classification [24, 25]. At present, a large number of researchers have used machine learning methods on atmospheric ducts and achieved good results [26–30]. Among them, a support vector machine (SVM) is widely used.…”

Section: Introductionmentioning

confidence: 99%

Research on evaporation duct height prediction based on back propagation neural network

Zhao

et al. 2020

IET Microwaves, Antennas & Propagation

View full text Add to dashboard Cite

Retrieve the evaporation duct height by least-squares support vector machine algorithm

Abstract: International audienc

Cited by 13 publications

References 2 publications

Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion

Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion

XGB Model : Research on Evaporation Duct Height Prediction Based on XGBoost Algorithm

Research on evaporation duct height prediction based on back propagation neural network

Contact Info

Product

Resources

About