Circularly Polarized Wave Scattering From Two-Dimensional Dielectric Rough Sea Surface

Guo, Lixin; Wang, and Qiang

doi:10.2528/pierm15090702

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…For circular polarization incident waves, the scattering coefficient curve for LR polarization falls between the curves of HH and VV polarizations. In addition, the scattering coefficient of LR polarization significantly exceeds that of RR polarization, which is because of the Brewster effect [37].…”

Section: Scattering Characteristics Analysis Of Sea Surfacementioning

confidence: 98%

Research on Circular Polarization Composite Scattering Characteristics of Sea Surface and Ship Target at GPS Frequency

Zhao,

Liao,

Liu

et al. 2024

PIER C

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The electromagnetic characteristics analysis of the scattering signals from targets, which usually exist or are hidden in the surrounding environment, is one of the necessary prerequisites for the reliable reception of echo signals. Utilizing the GNSS signals as an opportunistic illumination source for detecting maritime targets has vast development prospect and scientific application value. GNSS signals, including GPS signals, are the right-hand circular polarization waves at L-band. Therefore, in this study, a comprehensive electromagnetic composite scattering model is established under circular polarization, which encompasses sea surface scattering, target single scattering, target multiple scattering, and coupled scattering between the target and sea surface. Then, the research investigates the variation characteristics of different scattering components (including the scattering of sea surface, the first, second, and third-order scattering of target, the total scattering of target, the coupled scattering of target induced by the reflection waves from sea surface, and the coupled scattering of sea surface induced by the reflection waves from target) in the composite scene under different polarizations, incident angles, wind speeds, and headings. The results indicate that the scattering of sea surface under LR polarization (which means that the polarization states of scattering and incident wave are left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP), respectively) is significantly greater than that under RR polarization, while the opposite trend is observed for the target. Therefore, in the applications such as the detection and identification of ship targets on sea surface, it is better to choose the right-hand circular polarization channel to receive the scattering echo signal from target, which could effectively suppress the scattering echo of sea surface. These findings are of crucial significance in enhancing the effectiveness and accuracy of maritime target detection.

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Section: Scattering Characteristics Analysis Of Sea Surfacementioning

confidence: 98%

Research on Circular Polarization Composite Scattering Characteristics of Sea Surface and Ship Target at GPS Frequency

Zhao,

Liao,

Liu

et al. 2024

PIER C

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“…where the first subscript indicates transmit and the second is receive. For a RHC polarised radar wave incident on a smooth surface, the reflected wave can analytically be determined to be LHC if the IA is below Brewster's angle, and RHC if it is above [32]. For a mono-static radar, which measures the backscattered signal, simulation studies conducted by the authors of [32] show that at low to medium IA the LHC component dominates the signal for RHC incidence, and that this dominance decreases with increasing incidence angle and roughness.…”

Section: A the Circular Correlation Coefficientmentioning

confidence: 99%

Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data

Blix

Espeseth

Eltoft

2021

IEEE Trans. Geosci. Remote Sensing

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This work introduces a novel method which combines Machine Learning (ML) techniques with dual-polarimetric (dual-pol) SAR observations for estimating quad-polarimetric (quad-pol) parameters, which are presumed to contain geophysical sea ice information. In the training phase, the output parameters are generated from quad-polarimetric observations obtained by Radarsat-2 (RS2), and the corresponding input data consists of features obtained from overlapping dual-pol Sentinel-1 (S1) data. Then two, well recognized ML methods are studied to learn the functional relationship between the output and input data. These ML approaches are the Gaussian Process Regression (GPR) and Neural Network (NN) for regression models. The goal is to use the aforementioned ML techniques to generate Arctic sea ice information from freely available dual-pol observations acquired by S1, which can in general only be generated from quad-pol data.Eight overlapping RS2 and S1 scenes were used to train and test the GPR and NN models. Statistical regression performance measures were computed to evaluate the strength of the ML regression methods. Then two scenes were selected for further evaluation, where overlapping optical images were available as well. This allowed the visual interpretation of the maps estimated by the ML models. Finally, one of the methods was tested on an entire S1 scene to perform prediction on areas outside of the RS2 and S1 overlap.Our results indicate that the studied ML techniques can be utilized to increase the information retrieval capacity of the wide swath dual-pol S1 imagery, while embedding physical properties in the methodology.

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Analysis of GPS signals backscattered from a target on the sea surface

Ullo

Giangregorio

Bisceglie

et al. 2017

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Circularly Polarized Wave Scattering From Two-Dimensional Dielectric Rough Sea Surface

Cited by 3 publications

References 30 publications

Research on Circular Polarization Composite Scattering Characteristics of Sea Surface and Ship Target at GPS Frequency

Research on Circular Polarization Composite Scattering Characteristics of Sea Surface and Ship Target at GPS Frequency

Machine Learning for Arctic Sea Ice Physical Properties Estimation Using Dual-Polarimetric SAR Data

Analysis of GPS signals backscattered from a target on the sea surface

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