Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-07-02T22:23:51.722Z Has data issue: false hasContentIssue false

High-resolution signal processing techniques for through-the-wall imaging radar systems

Published online by Cambridge University Press:  29 April 2016

Ahmet Serdar Turk*
Affiliation:
Yildiz Technical University, Davutpasa Campus, 34220 Istanbul, Turkey. Phone: +90 212 383 58 80
Pinar Ozkan-Bakbak*
Affiliation:
Yildiz Technical University, Davutpasa Campus, 34220 Istanbul, Turkey. Phone: +90 212 383 58 80
Lutfiye Durak-Ata
Affiliation:
Istanbul Technical University, Ayazaga Campus, 34469 Istanbul, Turkey
Melek Orhan
Affiliation:
Yildiz Technical University, Davutpasa Campus, 34220 Istanbul, Turkey. Phone: +90 212 383 58 80
Mehmet Unal
Affiliation:
Yildiz Technical University, Davutpasa Campus, 34220 Istanbul, Turkey. Phone: +90 212 383 58 80
*
Corresponding authors:A. S. Turk and P. Ozkan-Bakbak Email: {asturk,pozkan}@yildiz.edu.tr
Corresponding authors:A. S. Turk and P. Ozkan-Bakbak Email: {asturk,pozkan}@yildiz.edu.tr

Abstract

Through-the-Wall Imaging is an ever-expanding area in which processing time, scanning time, vertical, and horizontal resolutions have been tried to improve. In this study, several methods are investigated to obtain efficient reconstruction of through-the-wall imaging radar signals with high resolution. Microwave radar signals, which are produced in YTU Microwave Laboratory, are processed by compressive sensing (CS). B and C scanned reflection data samples collected between 1 and 7 GHz frequency band are taken randomly at 1/4, 1/2 of whole amount and reconstructed by CS method. Considering the signal structure, 10 and 20 compressible Fourier coefficients are taken through CS to analyze the difference between them. In addition, we applied synthetic aperture radar (SAR) processing, also combined with SAR-Multiple Signal Classification over raw data. Experimental performance results are given and shown in the figures with high quality.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1] Daniels, D.J.: Surface penetrating radar. IEE Radar, Sonar, Navigation and Avionics Series 6, IEE, London, 1996.Google Scholar
[2] Turk, A.S.; Hocaoğlu, A.K.: Buried object detection. Encyclopedia RF Microw. Eng., 1 (2005), 541559.Google Scholar
[3] Borchert, O.; Aliman, M.; Glasmachers, A.: Directional borehole radar calibration. International Workshop on Advanced Ground Penetrating Radar, Italy, 2007.CrossRefGoogle Scholar
[4] Sahinkaya, D.S.A.; Turk, A.S.: UWB GPR for detection and identification of buried small objects. Proc. SPIE, 2004.Google Scholar
[5] Kong, F.N.; By, T.L.: Theory and performance of a GPR system which uses step frequency signals. J. Appl. Geophys., 33 (1993), 453–445.Google Scholar
[6] Thajudeen, C.; Hoorfar, A.; Ahmad, F.: Measured complex permittivity of walls with different hydration levels and the effect on power estimation of TWRI target returns. Prog. Electromag. Res. B, 30 (2011), 177199.Google Scholar
[7] Arai, L.; Shanker, M.S.: Signal processing of ground penetrating radar using spectral estimation techniques to estimate the position of buried targets. EURASIP J. Appl. Signal Process., 12 (2003), 11981209.Google Scholar
[8] Curlander, J.C.; McDounough, R.N.: Synthetic Aperture Radar, Systems and Signal Processing. John Wiley & Sons, New York, 1991.Google Scholar
[9] Chan, Y.K.; Koo, V.C.: An Introduction to synthetic aperture Radar (SAR). Prog. Electromag. Res. B, 2 (2008), 2760.Google Scholar
[10] Dogaru, T.; Le, C.: Recent investigations in sensing through the wall radar modeling. Antennas and Propagation Society Int. Symp., 2008. AP-S 2008, IEEE, 5–11 July 2008, 1–4.CrossRefGoogle Scholar
[11] Baraniuk, R.G.: Compressive sensing lecture notes. IEEE Signal Process. Mag., 24 (2007), 118121.Google Scholar
[12] Candès, E.; Romberg, J.; Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52 (2006), 489509.Google Scholar
[13] Candès, E.; Romberg, J.; Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math., 59 (2006), 12071223.CrossRefGoogle Scholar
[14] Donohoo, D.: Compressed sensing. IEEE Trans. Info. Theory, 52 (2006), 12891306.Google Scholar
[15] Candès, E.; Tao, T.: Near optimal signal recovery from random projections and universal encoding strategies. IEEE Trans. Info. Theory, 52 (2006), 54065425.Google Scholar
[16] Candès, E.; Wakin, M.: An introduction to compressive sampling. IEEE Signal Process. Mag., 25 (2008), 2130.Google Scholar
[17] Baraniuk, R.G.; Candès, E.; Nowak, R.; Vetterli, M.: Special section on compressive sampling. IEEE Signal Process. Mag., 25 (2008), 12101.Google Scholar
[18] Gurbuz, A.C.; McClellan, J.H.; Scott, W.R.: Compressive sensing for GPR imaging. Asilomar Conference on Signals, Systems, and Computers, 2007.Google Scholar
[19] Gurbuz, A.C.; McClellan, J.H.; Scott, W.R.: A compressive sensing data acquisition and imaging method for stepped frequency GPRs. IEEE Trans. Signal Process., 57 (2009), 26402650.Google Scholar
[20] Gurbuz, A.C.; McClellan, J.H.; Scott, W.R.: Compressive sensing for subsurface imaging using ground penetrating radars. Signal Process., 89 (2009), 19591972.Google Scholar
[21] Yoon, Y.; Amin, M.G.: Imaging of behind the wall targets using wide-band beam forming with compressive sensing. 15th Workshop on Statistical Signal Process., 9396, August 31-September 3 2009.Google Scholar
[22] Turk, A.S.; Keskin, A.K.; Senturk, M.D.: Dielectric loaded TEM horn-fed ridged horn antenna design for ultra wideband ground-penetrating impulse radar. Turkish J. Elect. Eng. Comput. Sci., 23 (2015), 14791488.Google Scholar
[23] Turk, A.S.; Ozkan-Bakbak, P.; Durak-Ata, L.; Orhan, M.; Unal, M.: Reconstruction of through-the-wall imaging radar signals by compressive sensing. Signal Processing Symp. (SPS 2015), Poland, 10–12 June 2015.CrossRefGoogle Scholar
[24] Grant, M.; Boyd, S.: Matlab software for disciplined convex programming (Web Page and Software) 2008. Available: http://cvxr.com/cvx/ Google Scholar
[25] Dogaru, T.; Nguyen, L.; Le, C.: Computer models of the human body signature for sensing through the wall radar applications. ARL-TR-4290, September 2007.Google Scholar