DocumentCode
31405
Title
A Generalized Approach for SAR and MIMO Radar Imaging of Building Interior Targets With Compressive Sensing
Author
Wenji Zhang ; Hoorfar, Ahmad
Author_Institution
Antenna Res. Lab., Villanova Univ., Villanova, PA, USA
Volume
14
fYear
2015
fDate
2015
Firstpage
1052
Lastpage
1055
Abstract
Most existing methods on compressive sensing (CS) for through-the-wall radar imaging (TWRI) are developed for monostatic synthetic aperture radar (SAR) and are only capable of target imaging behind a single-layer wall. In this letter, a generalized Green´s function-based approach for the imaging of targets behind single- or multilayered building walls with CS is proposed. The approach is applicable to both SAR and multiple-input-multiple-output (MIMO) radar. By exploiting the sparsity of the target space, a less cluttered high-resolution image can be achieved with far fewer measurements. The number of antenna elements in the MIMO radar system can be significantly reduced using the proposed approach, resulting in an overall reduction of the complexity and cost of MIMO radar for TWRI applications. Numerical results are presented to show that high-quality focused image can be achieved under various wall-target scenarios for both SAR and MIMO radar.
Keywords
Green´s function methods; MIMO radar; compressed sensing; radar imaging; synthetic aperture radar; MIMO radar imaging; SAR radar imaging; TWRI; compressive sensing; generalized Green´s function-based approach; monostatic synthetic aperture radar; multiple-input-multiple-output radar; through-the-wall radar imaging; Antenna measurements; Frequency measurement; Imaging; MIMO radar; Radar imaging; Receivers; Synthetic aperture radar; Compressive sensing (CS); multilayered wall; multiple-input–multiple-output (MIMO) radar; synthetic aperture radar (SAR); through-the-wall radar imaging (TWRI);
fLanguage
English
Journal_Title
Antennas and Wireless Propagation Letters, IEEE
Publisher
ieee
ISSN
1536-1225
Type
jour
DOI
10.1109/LAWP.2015.2394746
Filename
7017532
Link To Document