Canonical Scattering Feature Models for 3D and Bistatic SAR

Jackson, Julie Ann; Rigling, Brian D.; Moses, Randolph L.

doi:10.1109/taes.2010.5461639

Cited by 139 publications

(75 citation statements)

References 26 publications

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“…GTD-based parametric models not only provide a physically relevant representation of the scattering behavior but also conform to actual condition. In [30], bistatic scattering models for canonical scatterers, rectangular plate, dihedral, trihedral, cylinder, top-hat, etc., have been proposed, which offer benefits to area target spatial signature analysis.…”

Section: Canonical Area Target Spatial Signaturementioning

confidence: 99%

A Wall-Clutter Suppression Method Based on Spatial Signature in Mimo Through-the-Wall Radar Imaging

Zhang¹,

Lu²,

Zhou³

et al. 2013

PIER B

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Abstract-In through-the-wall radar imaging (TWRI), wall returns are often stronger than target returns, which make the targets behind walls invisible in the radar image. Spatial filtering that relies on the removal of the spatial zero-frequency components is a useful way for wall-clutter mitigation. Unfortunately, it applies to through-the-wall radar (TWR) with synthetic aperture array only. In this paper, a method based on spatial signature is proposed to suppress the wallclutter in multi-input and multi-output (MIMO) TWRI. Firstly, the traditional spatial filtering method is discussed, as well as the reasons for the inapplicability for MIMO TWR. Secondly, the wall and target spatial signatures based on MIMO array are analyzed, respectively. The results indicate that the former has stability and symmetry, whereas the latter not. Thirdly, according to the above differences, a new method, symmetry subtraction, is applied to describe the wallclutter suppression procedure. Finally, simulation results demonstrate that the proposed method is efficient in mitigating the wall returns and highlighting the targets.

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Section: Canonical Area Target Spatial Signaturementioning

confidence: 99%

A Wall-Clutter Suppression Method Based on Spatial Signature in Mimo Through-the-Wall Radar Imaging

Zhang¹,

Lu²,

Zhou³

et al. 2013

PIER B

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“…The dihedral PM in [6] is a function of the dihedral location, orientation, and size as well as the transmitter and receiver aspect angles and frequency. PMs are much more computationally efficient than numerical prediction methods but may be less accurate due to approximations used to derive compact PM equations.…”

Section: E Parametric Modelmentioning

confidence: 99%

“…Bistatic scattering is typically predicted using numerical methods, such as method of moments (MoM), finite integration technique (FIT), and shooting and bouncing rays (SBR). Recently, [5] and [6] have proposed computationally efficient analytic models to predict 3D bistatic scattering from a right-angle dihedrals. The model in [5] is a closed-form physical optics (CPO) solution, while the model in [6] is a simplified parametric model (PM) based on principle plane scattering.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, [5] and [6] have proposed computationally efficient analytic models to predict 3D bistatic scattering from a right-angle dihedrals. The model in [5] is a closed-form physical optics (CPO) solution, while the model in [6] is a simplified parametric model (PM) based on principle plane scattering. We compare the MoM, FIT, SBR, CPO, and PM predictions to data measured in the AFIT indoor range facility.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of measured and predicted bistatic scattering from a right-angle dihedral

Tempelis

Jussaume

Jackson

2011

2011 IEEE RadarCon (RADAR)

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Abstract-The bistatic radar cross section of a dihedral at three different orientations is measured and compared to five scattering predictions-method of moments (MoM), finite integration technique (FIT), shooting and bouncing rays (SBR), and two analytic models-closed-form PO (CPO) and a simplified parametric model. For many applications, accurate predictions in the mainlobe response are of most importance. We show that bistatic CPO and PM predictions have good fit to measured data in the mainlobe and are computationally more efficient than the numerical prediction methods.

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“…Containing rich scattering and polarization information, SAR imagery is widely used and can be formed regardless of time or weather [3]. Generally, target detection methods in this field can be divided into three categories: (1) the scattering center model-based detection algorithm [4]; (2) the statistical feature-based detection algorithm [5]; (3) the detection algorithm which introduces classical optical detection methods [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

Xiong

et al. 2018

IJGI

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This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately.

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Canonical Scattering Feature Models for 3D and Bistatic SAR

Cited by 139 publications

References 26 publications

A Wall-Clutter Suppression Method Based on Spatial Signature in Mimo Through-the-Wall Radar Imaging

A Wall-Clutter Suppression Method Based on Spatial Signature in Mimo Through-the-Wall Radar Imaging

Comparison of measured and predicted bistatic scattering from a right-angle dihedral

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

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