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Journal of Electrical and Computer Engineering
Volume 2013 (2013), Article ID 636972, 11 pages
http://dx.doi.org/10.1155/2013/636972
Research Article

Performance of 2D Compressive Sensing on Wide-Beam Through-the-Wall Imaging

CISS Department, Royal Military Academy, 30 Avenue de la Renaissance, 1000 Brussels, Belgium

Received 27 May 2013; Revised 18 July 2013; Accepted 24 July 2013

Academic Editor: Sandra Costanzo

Copyright © 2013 Edison Cristofani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Compressive sensing has become an accepted and powerful alternative to conventional data sampling schemes. Hardware simplicity, data, and measurement time reduction and simplified imagery are some of its most attractive strengths. This work aims at exploring the possibilities of using sparse vector recovery theory for actual engineering and defense- and security-oriented applications. Conventional through-the-wall imaging using a synthetic aperture configuration can also take advantage of compressive sensing by reducing data acquisition rates and omitting certain azimuth scanning positions. An ultra-wideband stepped frequency system carrying wide beam antennas performs through-the-wall imaging of a real scene, including a hollow concrete block wall and a corner reflector behind it. Random downsampling rates lower than those announced by Nyquist’s theorem both in the fast-time and azimuth domains are studied, as well as downsampling limitations for accurate imaging. Separate dictionaries are considered and modeled depending on the objects to be reconstructed: walls or point targets. Results show that an easy interpretation of through-the-wall scenes using the -norm and orthogonal matching pursuit algorithms is possible thanks to the simplification of the reconstructed scene, for which only as low as 25% of the conventional SAR data are needed.