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Near to far field transformation of RCS using a compressive sensing method

Larsson, Christer LU (2017) 2016 Antenna Measurement Techniques Association, AMTA 2016 In AMTA 2016 Proceedings
Abstract

Near field Inverse Synthetic Aperture Radar Cross Section (RCS) measurements are used in this study to obtain geometrically correct images of objects placed on a turntable. The images of the targets are processed using a method common in the compressive sensing field, Basis Pursuit Denoise. A near field model based on isotropic point scatterers is set up. The target model is naturally sparse and this ℓ1-minimization method works well to solve the inverse problem. The point scatterer solution is then used to obtain the far field RCS. The methods required for the imaging and the RCS extraction are described and evaluated. A comparison to image based near to far field methods utilizing conventional back projection is made. The... (More)

Near field Inverse Synthetic Aperture Radar Cross Section (RCS) measurements are used in this study to obtain geometrically correct images of objects placed on a turntable. The images of the targets are processed using a method common in the compressive sensing field, Basis Pursuit Denoise. A near field model based on isotropic point scatterers is set up. The target model is naturally sparse and this ℓ1-minimization method works well to solve the inverse problem. The point scatterer solution is then used to obtain the far field RCS. The methods required for the imaging and the RCS extraction are described and evaluated. A comparison to image based near to far field methods utilizing conventional back projection is made. The main advantage of the method presented in this paper is the absence of clutter, noise and side lobes in the solution of the inverse problem. Separate features in the images containing the point scatterers can be selected and a processing step can be performed to obtain the far field RCS of the full target or selected parts of the target.

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author
organization
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Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
AMTA 2016 Proceedings
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
2016 Antenna Measurement Techniques Association, AMTA 2016
external identifiers
  • scopus:85011051632
ISBN
9781509051793
DOI
10.1109/AMTAP.2016.7806301
language
English
LU publication?
yes
id
3882f1a2-3dc4-4d9e-9aa8-13b104c5f329
date added to LUP
2017-03-02 14:11:09
date last changed
2018-01-07 11:53:45
@inproceedings{3882f1a2-3dc4-4d9e-9aa8-13b104c5f329,
  abstract     = {<p>Near field Inverse Synthetic Aperture Radar Cross Section (RCS) measurements are used in this study to obtain geometrically correct images of objects placed on a turntable. The images of the targets are processed using a method common in the compressive sensing field, Basis Pursuit Denoise. A near field model based on isotropic point scatterers is set up. The target model is naturally sparse and this ℓ<sub>1</sub>-minimization method works well to solve the inverse problem. The point scatterer solution is then used to obtain the far field RCS. The methods required for the imaging and the RCS extraction are described and evaluated. A comparison to image based near to far field methods utilizing conventional back projection is made. The main advantage of the method presented in this paper is the absence of clutter, noise and side lobes in the solution of the inverse problem. Separate features in the images containing the point scatterers can be selected and a processing step can be performed to obtain the far field RCS of the full target or selected parts of the target.</p>},
  author       = {Larsson, Christer},
  booktitle    = {AMTA 2016 Proceedings},
  isbn         = {9781509051793},
  language     = {eng},
  month        = {01},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Near to far field transformation of RCS using a compressive sensing method},
  url          = {http://dx.doi.org/10.1109/AMTAP.2016.7806301},
  year         = {2017},
}