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Reseach Article

Study of Compressive Sensing on through Wall Imaging

by Abhay N. Gaikwad, Laxmikant K. Shevada
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 21
Year of Publication: 2013
Authors: Abhay N. Gaikwad, Laxmikant K. Shevada
10.5120/12670-9413

Abhay N. Gaikwad, Laxmikant K. Shevada . Study of Compressive Sensing on through Wall Imaging. International Journal of Computer Applications. 72, 21 ( June 2013), 45-49. DOI=10.5120/12670-9413

@article{ 10.5120/12670-9413,
author = { Abhay N. Gaikwad, Laxmikant K. Shevada },
title = { Study of Compressive Sensing on through Wall Imaging },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 21 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number21/12670-9413/ },
doi = { 10.5120/12670-9413 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:34.433790+05:30
%A Abhay N. Gaikwad
%A Laxmikant K. Shevada
%T Study of Compressive Sensing on through Wall Imaging
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 21
%P 45-49
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The theory of Compressive Sensing (CS) enables the reconstruction of sparse signals as well as image from small set of random measurements by solving primal dual interior point method for l1 minimization problem. This paper presents reconstruction of signal based on CS theory applied on experimental data. Reconstruction of A-scan and B-scan image with fewer samples is obtained without affecting quality. Reconstruction of range profile with A-scan data with random samples by inverse discrete frequency transform (IDFT) is achieved and then compares it with CS reconstructed range profile by computing peak signal to noise ratio (PSNR). It is observed that PSNR obtained with CS has good quality while using IDFT, it is degraded. Similar results are obtained for B-scan image.

References
  1. F. Ahmad & M. G. Amin. Through the wall radar imaging experiments. Proc. IEEE workshop on signal processing applications for public security & forensics SAFE'07, Washington DC, pp. 1-5, Apr. 2007.
  2. F. Ahmad, M. G. Amin & G. Mandapati. Auto focusing of through the wall radar imagery under unknown wall characteristics. IEEE trans. Image processing, vol. 16, no. 7, pp. 1785-1795, Jul. 2007.
  3. D. J. Taylor. Introduction to Ultra-wideband Radar Systems. CRC press 1995.
  4. M. Dehmollaian & K. . Sarabandi. Refocusing through building walls using synthetic aperture radar. IEEE trans. Geosciences & remote sensing, vol. 46,no. 6,pp. 1589-1599, Jun. 2008.
  5. R. Chandra, Abhay N Gaikwad, Dharmendra Singh & M J Nigam. An approach to remove the clutter & detect the target for ultra-wideband through wall imaging. Journal of geophysics & engg. , vol. no. 5, pp. 412-419, Oct. 2008.
  6. Cleve Moler. Magic reconstruction: Compressed sensing. Mathworks News & Notes
  7. M Richard G. Baraniuk. Compressive Sensing. 2007 IEEE Signal Processing Magazine
  8. Ali Cafer Gurbuz, James H. McClellan, Waymond R. Scott Jr. Compressive Sensing for GPR Imaging. 2007 IEEE Signal Processing Magazine
  9. Mehmet Ali Cagri Tuncer and Ali Cafer Gurbuz. Ground Reflection Removal in Compressive Sensing Ground Penetrating Radars. IEEE geosci. Remote sens. , vol. 9, no. 1, Jan. 2012.
  10. Mariví Tello Alonso, Paco López-Dekker, & Jordi J. Mallorquí. A Novel Strategy for Radar Imaging Based on Compressive Sensing. IEEE trans. on geosci. Remote sens. , vol. 48, no. 12, Dec. 2010.
  11. Jose L. Paredes, Gonzalo R. Arce, & Zhongmin Wang. Ultra-Wideband Compressed Sensing: Channel Estimation, IEEE journal of selected topics in signal processing, vol. 1, no. 3, Oct. 2007.
  12. Yeo-Sun Yoon, Moeness G. Amin. Through-the-wall radar imaging using compressive sensing along temporal frequency domain. 2010 IEEE Signal Processing Magazine.
  13. Qiong Huang, Lele Qu, Bingheng Wu, and Guangyou Fang. UWB Through-Wall Imaging Based on Compressive Sensing. IEEE transactions on geoscience and remote sensing, vol. 48, no. 3, march 2010
  14. Moeness G. Amin, Fauzia Ahmad, Wenji Zhang. Target RCS Exploitations in Compressive Sensing for Through Wall Imaging. 2010 IEEE signal processing magazine.
  15. Muhammed Duman , Ali Cafer Gurbuz. Through the wall imaging with compressive sensing and effects of unknown parameters to the performance, 2012 IEEE signal processing magazine.
  16. Andriyan Bayu Suksmono, Endon Bharata, Andrian Andaya Lestari, Member, Alexander G. Yarovoy, and Leo P. Ligthart, "Compressive Stepped-Frequency Continuous-Wave Ground-Penetrating Radar", IEEE Geoscience & Remote Sensing Letters, Vol. 7,No. 4,October 2007.
  17. Verma P. K. , Gaikwad A. N. , Singh D. and Nigam M. J. , Analysis of clutter reduction techniques for through wall imaging in UWB range, Progress in Electromagnetics Research B, 17, pp. 29-48, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

CS l1 PSNR Stepped frequency continuous wave (SFCW) through wall imaging (TWI)