SAR-ISAR Blending Using Compressed Sensing Methods
(2015) 37th Annual Symposium of the Antenna Measurements Techniques Association p.1-6- Abstract
- Inverse Synthetic Aperture Radar (ISAR) target
images are extracted using compressed sensing methods. The
extracted images are edited and merged into measured Synthetic
Aperture Radar (SAR) images. A noise free image of the target
is extracted from the Radar Cross Section (RCS) measurement
by using the Basis Pursuit Denoise (BPDN) method and then
solving for a model consisting of point scatterers. The target
signature point scatterers are then merged into a point scatterer
representation of the SAR background scene. This method means
that SAR images acquired in expensive airborne field trials can
be used efficiently to evaluate different targets and... (More) - Inverse Synthetic Aperture Radar (ISAR) target
images are extracted using compressed sensing methods. The
extracted images are edited and merged into measured Synthetic
Aperture Radar (SAR) images. A noise free image of the target
is extracted from the Radar Cross Section (RCS) measurement
by using the Basis Pursuit Denoise (BPDN) method and then
solving for a model consisting of point scatterers. The target
signature point scatterers are then merged into a point scatterer
representation of the SAR background scene. This method means
that SAR images acquired in expensive airborne field trials can
be used efficiently to evaluate different targets and camouflage
measured separately in a ground based setup. The method is
demonstrated with turntable measurements of a full scale target,
with and without camouflage, signature extraction and blending
into a SAR background. We find that the method provides an
efficient way of evaluating measured target signatures in SAR
backgrounds. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/8160870
- author
- Larsson, Christer LU and Jersblad, Johan
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- AMTA Proceedings
- pages
- 6 pages
- publisher
- Antenna Measurement Techniques Association
- conference name
- 37th Annual Symposium of the Antenna Measurements Techniques Association
- conference location
- Long Beach, United States
- conference dates
- 2015-10-13
- language
- English
- LU publication?
- yes
- id
- 15e4a5b4-08d6-4e40-b959-a0e1f4639d27 (old id 8160870)
- date added to LUP
- 2016-04-04 10:18:36
- date last changed
- 2018-11-21 20:58:02
@inproceedings{15e4a5b4-08d6-4e40-b959-a0e1f4639d27,
abstract = {{Inverse Synthetic Aperture Radar (ISAR) target<br/><br>
images are extracted using compressed sensing methods. The<br/><br>
extracted images are edited and merged into measured Synthetic<br/><br>
Aperture Radar (SAR) images. A noise free image of the target<br/><br>
is extracted from the Radar Cross Section (RCS) measurement<br/><br>
by using the Basis Pursuit Denoise (BPDN) method and then<br/><br>
solving for a model consisting of point scatterers. The target<br/><br>
signature point scatterers are then merged into a point scatterer<br/><br>
representation of the SAR background scene. This method means<br/><br>
that SAR images acquired in expensive airborne field trials can<br/><br>
be used efficiently to evaluate different targets and camouflage<br/><br>
measured separately in a ground based setup. The method is<br/><br>
demonstrated with turntable measurements of a full scale target,<br/><br>
with and without camouflage, signature extraction and blending<br/><br>
into a SAR background. We find that the method provides an<br/><br>
efficient way of evaluating measured target signatures in SAR<br/><br>
backgrounds.}},
author = {{Larsson, Christer and Jersblad, Johan}},
booktitle = {{AMTA Proceedings}},
language = {{eng}},
pages = {{1--6}},
publisher = {{Antenna Measurement Techniques Association}},
title = {{SAR-ISAR Blending Using Compressed Sensing Methods}},
url = {{https://lup.lub.lu.se/search/files/5509289/8160871.pdf}},
year = {{2015}},
}