Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-22T08:21:19.516Z Has data issue: false hasContentIssue false
  • English
  • Français

Determination of electromagnetic source localization with factorization method

Published online by Cambridge University Press:  23 August 2021

Nuri Gokmen Karakiraz Open the ORCID record for Nuri Gokmen Karakiraz [Opens in a new window] ,
Agah Oktay Ertay Open the ORCID record for Agah Oktay Ertay [Opens in a new window]  and
Ersin Göse
Nuri Gokmen Karakiraz*
Affiliation:
Electronics Engineering Department, National Defence University Hezârfen Aviation and Space Technologies Institute, 34149 Istanbul, Turkey
Agah Oktay Ertay
Affiliation:
Electrical Electronics Engineering Department, Erzincan Binali Yildirim University, 24002 Erzincan, Turkey
Ersin Göse
Affiliation:
Electronics Engineering Department, National Defence University Hezârfen Aviation and Space Technologies Institute, 34149 Istanbul, Turkey
*
Author for correspondence: Nuri Gokmen Karakiraz, E-mail: gkarakiraz@hho.msu.edu.tr
Get access Rights & Permissions [Opens in a new window]

Abstract

The factorization method (FM) is an attractive qualitative inverse scattering technique for the detection of geometrical features of unknown objects. This method depends on the selection of regularization parameters slightingly and has low calculation necessities. The aim of this work is to present a near-field FM for inverse source problems that have many applications. A modified test equation is obtained by converting the far-field term to Hankel's function. A different method has been proposed by manipulating the asymptotic approximation of Hankel's function in order to obtain near-field equations with incident angle and distance parameters. The novelty of this study is an integral equation based on the FM, which consists of multifrequency sparse near-field electric field measurements. We proved that the solution of the proposed integral equation gives information about the location of scatterers. The proposed algorithm is validated with simulation results and the capabilities of the presented method are assessed with several frequency regions and sources. Additionally, the presented method is compared with the direct sampling method in order to understand the performance of the proposed approach over a given scenario. The developed FM provides accurate results for electromagnetic source problems.

Keywords

Factorization methodinverse source problemmultifrequency
Type
Microwave Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Issue 7 , September 2022 , pp. 839 - 848
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

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

Pastorino, M (2010) Microwave Imaging. New York, NY, USA: John Wiley & Sons.10.1002/9780470602492CrossRefGoogle Scholar
Nikolova, NK (2011) Microwave imaging for breast cancer. IEEE Microwave Magazine 12, 78–94.10.1109/MMM.2011.942702CrossRefGoogle Scholar
Güren, O, Çayören, M, Ergene, LT and Akduman, I (2014) Surface impedance based microwave imaging method for breast cancer screening: contrast-enhanced scenario. Physics in Medicine & Biology 59, 5725.10.1088/0031-9155/59/19/5725CrossRefGoogle ScholarPubMed
Gurbuz, TU, Aslanyurek, B, Karabulut, EP and Akduman, I (2013) An efficient nonlinear imaging approach for dielectric objects buried under a rough surface. IEEE Transactions on Geoscience and Remote Sensing 52, 3013–3022.CrossRefGoogle Scholar
Ozdemir, O and Haddar, H (2010) Preprocessing the reciprocity gap sampling method in buried-object imaging experiments. IEEE Geoscience and Remote Sensing Letters 7, 756–760.10.1109/LGRS.2010.2047003CrossRefGoogle Scholar
Akıncı, MN, Çağlayan, T, Özgür, S, Alkaşı, U, Abbak, M and Çayören, M (2015) Experimental assessment of linear sampling and factorization methods for microwave imaging of concealed targets. International Journal of Antennas and Propagation 2015, 1–11.CrossRefGoogle Scholar
Pastorino, M (2015) Electromagnetic imaging: methods and applications in 2015 IEEE 15th Mediterranean Microwave Symposium (MMS), pp. 1–4.Google Scholar
Nikolova, NK (2017) Introduction to Microwave Imaging. UK: Cambridge University Press.10.1017/9781316084267CrossRefGoogle Scholar
Coşğun, S, Bilgin, E and Çören, M (2020) Microwave imaging of breast cancer with factorization method: SPIONs as contrast agent Medical Physics.CrossRefGoogle Scholar
Bertero, M and Boccacci, P (1998) Introduction to Inverse Problems in Imaging. UK: CRC Press.10.1887/0750304359CrossRefGoogle Scholar
Dogu, S, Akinci, MN, Cayoren, M and Akduman, I (2020) Truncated singular value decomposition for through-the-wall microwave imaging application. IET Microwaves, Antennas Propagation 14, 260–267.CrossRefGoogle Scholar
Berg, VN, Peter, M and Kleinman, RE (1997) A contrast source inversion method. Inverse Problems 13, 1607.10.1088/0266-5611/13/6/013CrossRefGoogle Scholar
Liu, J, Zhou, H, Chen, L and Ouyang, T (2020) Alternating direction method of multiplier for solving electromagnetic inverse scattering problems. International Journal of Microwave and Wireless Technologies 12, 790–796.10.1017/S175907872000015XCrossRefGoogle Scholar
Di Donato, L, Bevacqua, MT, Crocco, L and Isernia, T (2015) Inverse scattering via virtual experiments and contrast source regularization. IEEE Transactions on Antennas and Propagation 63, 1669–1677.CrossRefGoogle Scholar
Di Donato, L and Crocco, L (2015) Model-based quantitative cross-borehole GPR imaging via virtual experiments. IEEE Transactions on Geoscience and Remote Sensing 53, 4178–4185.10.1109/TGRS.2015.2392558CrossRefGoogle Scholar
Zakaria, A, Jeffrey, I, LoVetri, J and Zakaria, A (2013) Full-vectorial parallel finite-element contrast source inversion method. Progress In Electromagnetics Research 142, 463–483.10.2528/PIER13080706CrossRefGoogle Scholar
Bevacqua, MT, Crocco, L, Di Donato, L and Isernia, T (2014) Microwave imaging of nonweak targets via compressive sensing and virtual experiments. IEEE Antennas and Wireless Propagation Letters 14, 1035–1038.10.1109/LAWP.2014.2376612CrossRefGoogle Scholar
Bevacqua, MT and Scapaticci, R (2015) A compressive sensing approach for 3D breast cancer microwave imaging with magnetic nanoparticles as contrast agent. IEEE Transactions on Medical Imaging 35, 665–673.CrossRefGoogle ScholarPubMed
Colton, D, Haddar, H and Piana, M (2003) The linear sampling method in inverse electromagnetic scattering theory. Inverse Problems 19, S105.10.1088/0266-5611/19/6/057CrossRefGoogle Scholar
Cakoni, F, Colton, D and Monk, P (2011) The linear sampling method in inverse electromagnetic scattering, SIAM.CrossRefGoogle Scholar
Cakoni, F and Colton, D (2014) A Qualitative Approach to Inverse Scattering Theory. NY, USA: Springer.10.1007/978-1-4614-8827-9CrossRefGoogle Scholar
Dogu, S, Akinci, MN and Gose, E (2021) Experimental moving target imaging in a nonanechoic environment with linear sampling method. IEEE Geoscience and Remote Sensing Letters 18(3), 441–445.10.1109/LGRS.2020.2976594CrossRefGoogle Scholar
Dogu, S and Akinci, MN (2018) Assessment of linear sampling sethod and factorization method for through the wall imaging in 2018 26th Telecommunications Forum (TELFOR), pp. 420–425.Google Scholar
Bevacqua, MT, Abdollahi, N, Jeffrey, I, Isernia, T and LoVetri, J (2019) Sparse-aperture qualitative inverse scattering using a phase-delay-based frequency variation constraint 2019 IEEE Research and Applications of Photonics in Defense Conference (RAPID), pp. 1–4.Google Scholar
Ambrosanio, M, Bevacqua, MT, Pascazio, V and Isernia, T (2020) Qualitative imaging of experimental multistatic ground penetrating radar data 2020 14th European Conference on Antennas and Propagation (EuCAP), pp. 1–4.Google Scholar
Ambrosanio, M, Bevacqua, MT, Pascazio, V and Isernia, T (2020) Qualitative inverse scattering for sparse-aperture data collections using a phase-delay frequency variation constraint. IEEE Transactions on Antennas and Propagation 68, 7530–7540.Google Scholar
Bevacqua, MT, Abdollahi, N, Jeffrey, I, Isernia, T and LoVetri, J (2020) Qualitative techniques for generating spatial prior information for biomedical microwave imaging 2020 14th European Conference on Antennas and Propagation (EuCAP), pp. 1–4.Google Scholar
Colton, D and Kress, R (2019) Inverse Acoustic and Electromagnetic Scattering Theory. Switzerland AG: Springer Nature.10.1007/978-3-030-30351-8CrossRefGoogle Scholar
Akinci, MN, Cayoren, M and Akduman, I (2016) Near-field orthogonality sampling method for microwave imaging: theory and experimental verification. IEEE Transactions on Microwave Theory and Techniques 64, 2489–2501.10.1109/TMTT.2016.2585488CrossRefGoogle Scholar
Bevacqua, MT, Isernia, T, Palmeri, R, Akinci Mehmet, N and Crocco, L (2020) Physical insight unveils new imaging capabilities of orthogonality sampling method. IEEE Transactions on Antennas and Propagation 68, 4014–4021.10.1109/TAP.2019.2963229CrossRefGoogle Scholar
Kirsch, A (1998) Characterization of the shape of a scattering obstacle using the spectral data of the far field operator. Inverse problems 14, 1489.10.1088/0266-5611/14/6/009CrossRefGoogle Scholar
Kirsch, A (1999) Factorization of the far-field operator for the inhomogeneous medium case and an application in inverse scattering theory. Inverse problems 15, 413.10.1088/0266-5611/15/2/005CrossRefGoogle Scholar
Grinberg, N and Kirsch, A (2008) The Factorization Method for Inverse Problems. NY, USA: Oxford University Press.Google Scholar
Kirsch, A (2004) The factorization method for Maxwell's equations. Inverse Problems 20, S117.10.1088/0266-5611/20/6/S08CrossRefGoogle Scholar
Eskandari, MR, Dehmollaian, M and Safian, R (2014) Experimental investigation of factorization method as a qualitative approach for near-field microwave imaging. IEEE Antennas and Wireless Propagation Letters 13, 289–292.10.1109/LAWP.2014.2302077CrossRefGoogle Scholar
Akinci, MN, Caglayan, T, Ozgur, S, Alkaşı, U, Ahmadzay, H, Abbak, M, Çayören, M and Akduman, I (2015) Qualitative microwave imaging with scattering parameters measurements. IEEE Transactions on Microwave Theory and Techniques 63, 2730–2740.10.1109/TMTT.2015.2451611CrossRefGoogle Scholar
Griesmaier, R and Schmiedecke, C (2017) A factorization method for multifrequency inverse source problems with sparse far field measurements. SIAM Journal on Imaging Sciences 10, 2119–2139.CrossRefGoogle Scholar
Alzaalig, A, Hu, G, Liu, X and Sun, J (2020) Fast acoustic source imaging using multi-frequency sparse data. Inverse Problems 36, 025009.10.1088/1361-6420/ab4aecCrossRefGoogle Scholar