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An autofocusing method for imaging the targets for TWI radar systems with correction of thickness and dielectric constant of wall

Published online by Cambridge University Press:  09 November 2018

Sandeep Kaushal
Affiliation:
Department of Electronics and Communication Engineering, ACET, Amritsar, India
Bambam Kumar
Affiliation:
Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Dharmendra Singh*
Affiliation:
Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
*
Author for correspondence: Dharmendra Singh, E-mail: dharmfec@gmail.com

Abstract

In through the wall imaging systems, wall parameters like its thickness and dielectric constant play an important role in the true and correct image formation of an object behind the wall made of various materials like brick cement, wood, plastic, etc. Incorrect estimation of these parameters leads to dislocation of the object and smearing or blurriness of the image too. A new autofocusing technique for a stepped frequency continuous wave -based radar at the frequency of 1–3 Ghz has been developed that corrects the wall's parameters like its thickness and dielectric constant and provides a better focused image of the target. For this purpose, a peak signal to noise ratio -based autofocusing technique has been developed by using curve fitting and the genetic algorithm. It is observed that the proposed technique has capability to focus the image up to good extent.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2018 

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References

1.Soldovieri, F, Prisco, G and Solimene, R (2008) A multi-array tomographic approach for trough-wall imaging. IEEE Transactions on Geoscience and Remote Sensing 46, 11921199.Google Scholar
2.Soldovieri, F and Solimene, R (2007) Through-wall imaging via a linear inverse scattering algorithm. IEEE Geoscience and Remote Sensing Letters 4, 513517.Google Scholar
3.Yoon, Y-S and Amin, MG (2008) High-resolution through-the-wall radar imaging using beamspace MUSIC. IEEE Transactions on Antennas and Propagation 56, 17631774.Google Scholar
4.Dehmollaian, M and Sarabandi, K (2008) Refocusing through building walls using synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing 46, 15891599.Google Scholar
5.Wang, G, Amin, MG and Zhang, Y (2006) New approach for target locations in the presence of wall ambiguities. IEEE Transactions on Aerospace and Electronic Systems 42, 301315.Google Scholar
6.Ahmad, F, Amin, MG and Mandapati, G (2007) Autofocusing of through the-wall radar imagery under unknown wall characteristics. IEEE Transactions on Image Processing 16, 17851795.Google Scholar
7.Li, L, Zhang, W and Li, F (2010) A novel auto focusing approach for real time through wall imaging under unknown wall characteristics. IEEE Transactions on Geosciences and Remote Sensing 48, 423431.Google Scholar
8.Jin, T, Chen, B and Zhou, Z (2013) Image-domain estimation of wall parameters for autofocusing of through-the-wall SAR imagery. IEEE Transactions on Geoscience and Remote Sensing 51, 18361843.Google Scholar
9.Verma, PK, Gaikwad, AN, Singh, D and Nigam, MJ (2009) Analysis of clutter reduction techniques for through wall imaging in UWB range. Progress in Electromagnetic Research B 17, 2948.Google Scholar
10.Kaushal, S and Singh, D (2015) An Alternative approach for wall parameters estimation in through wall imaging system. IEEE Conference RAECE-2015, Roorkee, pp. 8591.Google Scholar
11.Agarwal, S, Bisht, A, Singh, D and Pathak, NP (2014) A novel neural network based image reconstruction model with scale and rotation invariance for target identification and identification for active millimetre wave imaging. Journal of Infrared, Millimetre, and Terahertz Waves 35, 10451067.Google Scholar
12.Qiu, X, Hu, D and Ding, C (2008) An Omega-K algorithm with phase error compensation for bistatic SAR of a translational invariant case. IEEE Transactions on Geosciences and Remote Sensing 46, 22242232.Google Scholar
13.Rau, R and McClellan, JH (2000) Analytic models and postprocessing techniques for UWB SAR. IEEE Transactions on Aerospace and Electronic Systems 36, 10581074.Google Scholar
14.Wang, G and Amin, MG (2006) Through unknown walls using different standoff distances. IEEE Transactions On Signal Processing 54, 40154025.Google Scholar
15.Debes, C, Hahn, J, Zoubir, AM and Amin, MG (2011) Target discrimination and classification in through-the-wall radar imaging. IEEE Transactions on Signal Processing 59, 46644676.Google Scholar
16.Ahmad, F, Amin, MG and Kassam, SA (2005) Synthetic aperture beamformer for imaging through a single wall. IEEE Transactions on Aerospace and Electronic Systems 41, 271283.Google Scholar
17.Smith Steven, W (1999) The Scientist and Engineer's Guide to Digital Signal Processing, 2nd Edn. San Diego, CA: California Technical Publishing.Google Scholar
18.Kumar, B, Upadhyay, R and Singh, D (2017) Development of an adaptive approach for identification of targets (match box, pocket diary and cigarette box) under the cloth with MMW imaging system. Progress in Electromagnetics Research B 77, 3755.Google Scholar
19.Capelli, F, Riba, J-R, Rupérez, E and Sanllehí, J (2017) A genetic-algorithm-optimized fractal model to predict the constriction resistance from surface roughness measurements. IEEE Transactions on Instrumentation and Measurement 66, 24372447.Google Scholar
20.Mahlab, U, Shamir, J and Caulfield, HJ (1991) Genetic algorithm for optical pattern recognition. Optical Letter 16, 648650.Google Scholar