Compressive tomographic radar imaging with total variation regularization

Dehong Liu; Ulugbek S. Kamilov; Petros T. Boufounos

doi:10.1109/CoSeRa.2016.7745712

Compressive tomographic radar imaging with total variation regularization

Dehong Liu
, Ulugbek S. Kamilov
, Petros T. Boufounos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

We consider the problem of compressive imaging of a three-dimensional (3D) scene using multiple observations collected from parallel baselines, formed by monostatic sensors moving in space. In particular, we present a novel iterative imaging method based on the Omega-K algorithm with edgepreserving 3D total variation (TV) regularization. The method combines joint processing of multi-baseline data with TV minimization in a computationally efficient way, thus enabling highresolution imaging of the reflectivity map of the scene. We demonstrate the potential of our method through numerical evaluations on simulated data with noise.

Original language	English
Title of host publication	2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	120-123
Number of pages	4
ISBN (Electronic)	9781509029204
DOIs	https://doi.org/10.1109/CoSeRa.2016.7745712
State	Published - Nov 15 2016
Event	4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016 - Aachen, Germany Duration: Sep 19 2016 → Sep 23 2016

Publication series

Name	2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016

Conference

Conference	4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016
Country/Territory	Germany
City	Aachen
Period	09/19/16 → 09/23/16

Keywords

compressive sensing
high resolution imaging
omega-k imaging
three-dimensional imaging
total variation

Access to Document

10.1109/CoSeRa.2016.7745712

Cite this

Liu, D., Kamilov, U. S., & Boufounos, P. T. (2016). Compressive tomographic radar imaging with total variation regularization. In 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016 (pp. 120-123). Article 7745712 (2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CoSeRa.2016.7745712

Liu, Dehong ; Kamilov, Ulugbek S. ; Boufounos, Petros T. / Compressive tomographic radar imaging with total variation regularization. 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 120-123 (2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016).

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title = "Compressive tomographic radar imaging with total variation regularization",

abstract = "We consider the problem of compressive imaging of a three-dimensional (3D) scene using multiple observations collected from parallel baselines, formed by monostatic sensors moving in space. In particular, we present a novel iterative imaging method based on the Omega-K algorithm with edgepreserving 3D total variation (TV) regularization. The method combines joint processing of multi-baseline data with TV minimization in a computationally efficient way, thus enabling highresolution imaging of the reflectivity map of the scene. We demonstrate the potential of our method through numerical evaluations on simulated data with noise.",

keywords = "compressive sensing, high resolution imaging, omega-k imaging, three-dimensional imaging, total variation",

author = "Dehong Liu and Kamilov, \{Ulugbek S.\} and Boufounos, \{Petros T.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016 ; Conference date: 19-09-2016 Through 23-09-2016",

year = "2016",

month = nov,

day = "15",

doi = "10.1109/CoSeRa.2016.7745712",

language = "English",

series = "2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "120--123",

booktitle = "2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016",

}

Liu, D, Kamilov, US & Boufounos, PT 2016, Compressive tomographic radar imaging with total variation regularization. in 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016., 7745712, 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016, Institute of Electrical and Electronics Engineers Inc., pp. 120-123, 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016, Aachen, Germany, 09/19/16. https://doi.org/10.1109/CoSeRa.2016.7745712

Compressive tomographic radar imaging with total variation regularization. / Liu, Dehong; Kamilov, Ulugbek S.; Boufounos, Petros T.
2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 120-123 7745712 (2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Compressive tomographic radar imaging with total variation regularization

AU - Liu, Dehong

AU - Kamilov, Ulugbek S.

AU - Boufounos, Petros T.

PY - 2016/11/15

Y1 - 2016/11/15

N2 - We consider the problem of compressive imaging of a three-dimensional (3D) scene using multiple observations collected from parallel baselines, formed by monostatic sensors moving in space. In particular, we present a novel iterative imaging method based on the Omega-K algorithm with edgepreserving 3D total variation (TV) regularization. The method combines joint processing of multi-baseline data with TV minimization in a computationally efficient way, thus enabling highresolution imaging of the reflectivity map of the scene. We demonstrate the potential of our method through numerical evaluations on simulated data with noise.

AB - We consider the problem of compressive imaging of a three-dimensional (3D) scene using multiple observations collected from parallel baselines, formed by monostatic sensors moving in space. In particular, we present a novel iterative imaging method based on the Omega-K algorithm with edgepreserving 3D total variation (TV) regularization. The method combines joint processing of multi-baseline data with TV minimization in a computationally efficient way, thus enabling highresolution imaging of the reflectivity map of the scene. We demonstrate the potential of our method through numerical evaluations on simulated data with noise.

KW - compressive sensing

KW - high resolution imaging

KW - omega-k imaging

KW - three-dimensional imaging

KW - total variation

UR - https://www.scopus.com/pages/publications/85002912313

U2 - 10.1109/CoSeRa.2016.7745712

DO - 10.1109/CoSeRa.2016.7745712

M3 - Conference contribution

AN - SCOPUS:85002912313

T3 - 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016

SP - 120

EP - 123

BT - 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016

Y2 - 19 September 2016 through 23 September 2016

ER -

Liu D, Kamilov US, Boufounos PT. Compressive tomographic radar imaging with total variation regularization. In 2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 120-123. 7745712. (2016 4th International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, CoSeRa 2016). doi: 10.1109/CoSeRa.2016.7745712

Compressive tomographic radar imaging with total variation regularization

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