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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - A Singular Value Decomposition Based Approach for Classifying Concealed Objects in Short Range Polarimetric Radar Imaging
摘要： In current research one of the main challenges in short range synthetic aperture radar (SAR) is electrically small structures and objects, which tend to unclear reinforced or through the wall objects, object orientation angle, and obscure contribution to extract the position of concealed multiple small objects. In this paper, ultra-wide-band (UWB) polarimetric radar was used to study reinforced objects and for estimation of object angle at short range. Electrically small 1D periodic mesh, 2D periodic meshes and di?erently oriented small objects or meshes could not be distinguished in conventional SAR images. A radar system with transmit and receive antennae mounted on a two dimensional scanning grid was used. The aim is non-destructive testing of built structures, in concrete slab manufacturing and for use in the renovation process. UWB short range radar data and images corresponding to di?erent polarization states were analysed by using singular value decomposition (SVD). To perform decomposition, the proposed approach applies SVD to image data matrices produced from the back projection algorithm (BPA) to classify the di?erent objects and identify the object angle. Then, sets of singular-components of di?erent polarization states are analysed to classify objects. Also, the BPA algorithm is performed to construct the object images from the polarimetric radar signals. The object re?ection varied with the polarimetric state of the UWB radar, which contributes to di?erent object signatures (i.e., object intensity) since the object signature depends on the orientation, the size, and the number of objects. Object orientation with respect to the radar system and object anisotropy could be determined from the ratio of the di?erent polarimetric singular-components. This proposed complex data analysis method demonstrates the usefulness of the SVD using BPA in extracting more information about and for classifying an object.
关键词： back projection algorithm (BPA),object classification,ultra-wide-band (UWB) polarimetric radar,Synthetic aperture radar (SAR),singular value decomposition (SVD)
更新于2025-09-19 17:13:59
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Soil Moisture Estimation by Linear Regression from Smap Polarimetric Radar Data with Aquarius Derived Coefficients
摘要： Algorithms for soil moisture estimation from radars conventionally use substantial amounts of ancillary data to parametrize complex electromagnetic models. In contrast, we describe radar data of a vegetated scene as a linear function of soil moisture. This eliminates the dependence on ancillary data while providing reasonable global soil moisture estimates. We derive two polarization dependent coefficients of a linear model on the basis of spatial and temporal similarity at a global scale from nearly 4 years of L-band Aquarius radar and radiometer derived soil moisture data. These global coefficients are then used to derive soil moisture from 2.5 months of L-band SMAP radar data. The resulting soil moisture estimates are evaluated with the SMAP Level 2 radiometer-only soil moisture product.
关键词： Soil moisture,synthetic aperture radar (SAR),time series,polarimetric radar
更新于2025-09-10 09:29:36
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[IEEE 2018 19th International Radar Symposium (IRS) - Bonn, Germany (2018.6.20-2018.6.22)] 2018 19th International Radar Symposium (IRS) - Polarization Scattering Matrix Estimation for Moving Targets using Measurement Selection
摘要： In this paper, we deal with the problem of polarization scattering matrix (PSM) estimation for moving targets. To this end, we first formulate the measurement model for time-sharing polarimetric radar, and then jointly examine the influence of target motion on the scattering matrix estimation. For estimation purposes, we assume the target is deterministic during the coherent processing interval (CPI). Thus, there is a set of observation data with the same scattering matrix. As the performance of pulse integration (PI) declines when the target is in motion, we derive a method using optimal selection (OS) to estimate the moving target PSM. Compared with the PI, the proposed method shows excellent performance, and numerical results verify its validity.
关键词： pulse integration,polarization scattering matrix,moving targets,time-sharing polarimetric radar,optimal selection
更新于2025-09-09 09:28:46
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Retrieval of Polarimetric Azimuthal Angular Characteristics via the Application of Target Decomposition to Spectral Domain Circular SAR Images
摘要： In this paper, we discuss the application of polarimetric target decomposition (TD) theorems to circular synthetic aperture radar (CSAR) images to retrieve the polarimetric azimuthal angular characteristics of the targets. In CSAR systems, radar-carrying aircraft moves along a circular path. The antenna beam is pointed toward the center of the circular path during the data acquisition to irradiate the spotlighted area from various azimuthal or aspect angles. Therefore, the resultant polarimetric CSAR images contain information about the polarimetric scattering mechanisms of the targets at each aspect angles. To recover this information, we propose the use of spectral decomposition of CSAR images in combination with several TD algorithms. The spectral domain CSAR images can be viewed as the azimuthal angular spectrum images. Thus, the application of TD algorithms to the spectral domain images is expected to be able to obtain the angular dependence and polarimetric scattering properties of the targets simultaneously. We carried out a simple numerical simulation and an indoor laboratory experiment to validate the proposed TD scheme.
关键词： Circular SAR (CSAR),target decomposition (TD),polarimetric radar,spatial frequency spectrum
更新于2025-09-09 09:28:46