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Performance comparison of total variation minimization and group sparse reconstructions for extended target imaging in multilayered dielectric media
Author(s): Fauzia Ahmad; Wenji Zhang; Ahmad Hoorfar
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Paper Abstract

Imaging of targets embedded in multilayered dielectric media has attracted growing interest in microwave remote sensing, nondestructive testing, ground penetrating radar, and urban sensing. Compressive sensing has been successfully applied in the aforementioned applications for efficient target imaging, leading to prompt actionable intelligence. Recently, a total variation minimization (TVM) based approach was proposed, which offers superior performance over standard L1- minimization based sparse reconstruction in terms of target shape reconstruction and distinguishing closely-spaced point targets from an extended target. Alternatively, group sparse reconstruction (GSR) schemes can also be employed to account for target extent. In this paper, we provide a performance comparison between TVM and GSR schemes for extended target imaging in multi-layered media using numerical electromagnetic data.

Paper Details

Date Published: 14 May 2018
PDF: 7 pages
Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 106580I (14 May 2018); doi: 10.1117/12.2306692
Show Author Affiliations
Fauzia Ahmad, Temple Univ. (United States)
Wenji Zhang, Villanova Univ. (United States)
Ahmad Hoorfar, Villanova Univ. (United States)


Published in SPIE Proceedings Vol. 10658:
Compressive Sensing VII: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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