Electromagnetic imaging using compressive sensing

Marija M. Nikolić; Gongguo Tang; Antonije Djordjević; Arye Nehorai

doi:10.1109/ALLERTON.2010.5707126

Electromagnetic imaging using compressive sensing

Marija M. Nikolić, Gongguo Tang, Antonije Djordjević, Arye Nehorai

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

1 Scopus citations

Abstract

We develop a near-field compressive sensing (CS) estimation scheme for localizing scattering objects in vacuum. The potential of CS for localizing sparse targets was demonstrated in previous work. We extend the standard far-field approach to near-field scenarios by employing the electric field integral equation to capture the mutual interference among targets. We show that the advanced modeling improves the capability to resolve closely spaced targets. We compare the performance of our algorithm with the performances of CS applied to point targets and beamforming. In this paper, we consider two-dimensional (2D) scatterers. However, the results and conclusions can be extended to three-dimensional (3D) problems.

Original language	English
Title of host publication	2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010
Pages	1737-1741
Number of pages	5
DOIs	https://doi.org/10.1109/ALLERTON.2010.5707126
State	Published - 2010
Event	48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010 - Monticello, IL, United States Duration: Sep 29 2010 → Oct 1 2010

Publication series

Name	2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010

Conference

Conference	48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010
Country/Territory	United States
City	Monticello, IL
Period	09/29/10 → 10/1/10

Keywords

Compressive sensing
Inverse scattering
Radar
Sparse signal processing

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10.1109/ALLERTON.2010.5707126

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Nikolić, M. M., Tang, G., Djordjević, A., & Nehorai, A. (2010). Electromagnetic imaging using compressive sensing. In 2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010 (pp. 1737-1741). Article 5707126 (2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010). https://doi.org/10.1109/ALLERTON.2010.5707126

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title = "Electromagnetic imaging using compressive sensing",

abstract = "We develop a near-field compressive sensing (CS) estimation scheme for localizing scattering objects in vacuum. The potential of CS for localizing sparse targets was demonstrated in previous work. We extend the standard far-field approach to near-field scenarios by employing the electric field integral equation to capture the mutual interference among targets. We show that the advanced modeling improves the capability to resolve closely spaced targets. We compare the performance of our algorithm with the performances of CS applied to point targets and beamforming. In this paper, we consider two-dimensional (2D) scatterers. However, the results and conclusions can be extended to three-dimensional (3D) problems.",

keywords = "Compressive sensing, Inverse scattering, Radar, Sparse signal processing",

author = "Nikoli{\'c}, {Marija M.} and Gongguo Tang and Antonije Djordjevi{\'c} and Arye Nehorai",

year = "2010",

doi = "10.1109/ALLERTON.2010.5707126",

language = "English",

isbn = "9781424482146",

series = "2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010",

pages = "1737--1741",

booktitle = "2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010",

note = "48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010 ; Conference date: 29-09-2010 Through 01-10-2010",

}

Nikolić, MM, Tang, G, Djordjević, A & Nehorai, A 2010, Electromagnetic imaging using compressive sensing. in 2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010., 5707126, 2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010, pp. 1737-1741, 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010, Monticello, IL, United States, 09/29/10. https://doi.org/10.1109/ALLERTON.2010.5707126

Electromagnetic imaging using compressive sensing. / Nikolić, Marija M.; Tang, Gongguo; Djordjević, Antonije et al.
2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010. 2010. p. 1737-1741 5707126 (2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010).

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

TY - GEN

T1 - Electromagnetic imaging using compressive sensing

AU - Nikolić, Marija M.

AU - Tang, Gongguo

AU - Djordjević, Antonije

AU - Nehorai, Arye

PY - 2010

Y1 - 2010

N2 - We develop a near-field compressive sensing (CS) estimation scheme for localizing scattering objects in vacuum. The potential of CS for localizing sparse targets was demonstrated in previous work. We extend the standard far-field approach to near-field scenarios by employing the electric field integral equation to capture the mutual interference among targets. We show that the advanced modeling improves the capability to resolve closely spaced targets. We compare the performance of our algorithm with the performances of CS applied to point targets and beamforming. In this paper, we consider two-dimensional (2D) scatterers. However, the results and conclusions can be extended to three-dimensional (3D) problems.

AB - We develop a near-field compressive sensing (CS) estimation scheme for localizing scattering objects in vacuum. The potential of CS for localizing sparse targets was demonstrated in previous work. We extend the standard far-field approach to near-field scenarios by employing the electric field integral equation to capture the mutual interference among targets. We show that the advanced modeling improves the capability to resolve closely spaced targets. We compare the performance of our algorithm with the performances of CS applied to point targets and beamforming. In this paper, we consider two-dimensional (2D) scatterers. However, the results and conclusions can be extended to three-dimensional (3D) problems.

KW - Compressive sensing

KW - Inverse scattering

KW - Radar

KW - Sparse signal processing

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DO - 10.1109/ALLERTON.2010.5707126

M3 - Conference contribution

AN - SCOPUS:79952385831

SN - 9781424482146

T3 - 2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010

SP - 1737

EP - 1741

BT - 2010 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010

T2 - 48th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2010

Y2 - 29 September 2010 through 1 October 2010

ER -

Electromagnetic imaging using compressive sensing

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