Research article Special Issues

Real-time tracking of moving objects from scattering matrix in real-world microwave imaging

  • Received: 26 February 2024 Revised: 27 March 2024 Accepted: 07 April 2024 Published: 12 April 2024
  • MSC : 78A46

  • The problem of the real-time microwave imaging of small, moving objects from a scattering matrix without diagonal elements, whose elements are measured scattering parameters, is considered herein. An imaging algorithm based on a Kirchhoff migration operated at single frequency is designed, and its mathematical structure is investigated by establishing a relationship with an infinite series of Bessel functions of integer order and antenna configuration. This is based on the application of the Born approximation to the scattering parameters of small objects. The structure explains the reason for the detection of moving objects via a designed imaging function and supplies some of its properties. To demonstrate the strengths and weaknesses of the proposed algorithm, various simulations with real-data are conducted.

    Citation: Seong-Ho Son, Kwang-Jae Lee, Won-Kwang Park. Real-time tracking of moving objects from scattering matrix in real-world microwave imaging[J]. AIMS Mathematics, 2024, 9(6): 13570-13588. doi: 10.3934/math.2024662

    Related Papers:

  • The problem of the real-time microwave imaging of small, moving objects from a scattering matrix without diagonal elements, whose elements are measured scattering parameters, is considered herein. An imaging algorithm based on a Kirchhoff migration operated at single frequency is designed, and its mathematical structure is investigated by establishing a relationship with an infinite series of Bessel functions of integer order and antenna configuration. This is based on the application of the Born approximation to the scattering parameters of small objects. The structure explains the reason for the detection of moving objects via a designed imaging function and supplies some of its properties. To demonstrate the strengths and weaknesses of the proposed algorithm, various simulations with real-data are conducted.



    加载中


    [1] K. Agarwal, X. Chen, Y. Zhong, A multipole-expansion based linear sampling method for solving inverse scattering problems, Opt. Express, 18 (2010), 6366–6381. https://doi.org/10.1364/OE.18.006366 doi: 10.1364/OE.18.006366
    [2] H. Ammari, J. Garnier, H. Kang, M. Lim, K. Sølna, Multistatic imaging of extended targets, SIAM J. Imaging Sci., 5 (2012), 564–600. https://doi.org/10.1137/10080631X doi: 10.1137/10080631X
    [3] H. Ammari, J. Garnier, H. Kang, W.-K. Park, K. Sølna, Imaging schemes for perfectly conducting cracks, SIAM J. Appl. Math., 71 (2011), 68–91. https://doi.org/10.1137/100800130 doi: 10.1137/100800130
    [4] H. Ammari, E. Iakovleva, D. Lesselier, G. Perrusson, MUSIC type electromagnetic imaging of a collection of small three-dimensional inclusions, SIAM J. Sci. Comput., 29 (2007), 674–709. https://doi.org/10.1137/050640655 doi: 10.1137/050640655
    [5] T. Atay, M. Kaplan, Y. Kilic, N. Karapinar, A-Track: A new approach for detection of moving objects in FITS images, Comput. Phys. Commun., 207 (2016), 524–530. https://doi.org/10.1016/j.cpc.2016.07.023 doi: 10.1016/j.cpc.2016.07.023
    [6] E. J. Baranoski, Through-wall imaging: historical perspective and future directions, J. Franklin Inst., 345 (2008), 556–569. https://doi.org/10.1016/j.jfranklin.2008.01.005 doi: 10.1016/j.jfranklin.2008.01.005
    [7] M. Bonnet, Fast identification of cracks using higher-order topological sensitivity for 2-D potential problems, Eng. Anal. Bound. Elem., 35 (2011), 223–235. https://doi.org/10.1016/j.enganabound.2010.08.007 doi: 10.1016/j.enganabound.2010.08.007
    [8] C. Cai, W. Liu, J. S. Fu, Y. Lu, A new approach for ground moving target indication in foliage environment, Signal Process., 86 (2006), 84–97. https://doi.org/10.1016/j.sigpro.2005.04.011 doi: 10.1016/j.sigpro.2005.04.011
    [9] E. Castro-Camus, M. Koch, D. M. Mittleman, Recent advances in terahertz imaging: 1999 to 2021, Appl. Phys. B, 128 (2022), 12. https://doi.org/10.1007/s00340-021-07732-4 doi: 10.1007/s00340-021-07732-4
    [10] L. Collins, P. Gao, D. Schofield, J. P. Moulton, L. C. Majakowsky, D. M. Reidy, et al., A statistical approach to landmine detection using broadband electromagnetic data, IEEE Trans. Geosci. Remote, 40 (2002), 950–962. https://doi.org/10.1109/TGRS.2002.1006387 doi: 10.1109/TGRS.2002.1006387
    [11] D. Colton, H. Haddar, P. Monk, The linear sampling method for solving the electromagnetic inverse scattering problem, SIAM J. Sci. Comput., 24 (2002), 719–731. https://doi.org/10.1137/S1064827501390467 doi: 10.1137/S1064827501390467
    [12] D. Colton, R. Kress, Inverse acoustic and electromagnetic scattering problems, New York: Springer, 1998. https://doi.org/10.1007/978-1-4614-4942-3
    [13] S. Coşğun, E. Bilgin, M. Çayören, Microwave imaging of breast cancer with factorization method: SPIONs as contrast agent, Med. Phys., 47 (2020), 3113–3122. https://doi.org/10.1002/mp.14156 doi: 10.1002/mp.14156
    [14] H. Diao, H. Liu, L. Wang, On generalized Holmgren's principle to the Lamé operator with applications to inverse elastic problems, Calc. Var., 59 (2020), 179. https://doi.org/10.1007/s00526-020-01830-5 doi: 10.1007/s00526-020-01830-5
    [15] J. R. Fienup, Detecting moving targets in SAR imagery by focusing, IEEE Trans. Aero. Elec. Syst., 37 (2001), 794–809. https://doi.org/10.1109/7.953237 doi: 10.1109/7.953237
    [16] A. Foudazix, A. Mirala, M. T. Ghasr, K. M. Donnell, Active microwave thermography for nondestructive evaluation of surface cracks in metal structures, IEEE Trans. Instrum. Meas., 68 (2019), 576–585. https://doi.org/10.1109/TIM.2018.2843601 doi: 10.1109/TIM.2018.2843601
    [17] A. Franchois, C. Pichot, Microwave imaging-complex permittivity reconstruction with a Levenberg-Marquardt method, IEEE Trans. Antenn. Propag., 45 (1997), 203–215. https://doi.org/10.1109/8.560338 doi: 10.1109/8.560338
    [18] B. B. Guzina, F. Pourahmadian, Why the high-frequency inverse scattering by topological sensitivity may work, Proc. R. Soc. A, 471 (2015), 20150187. https://doi.org/10.1098/rspa.2015.0187 doi: 10.1098/rspa.2015.0187
    [19] H. Haddar, P. Monk, The linear sampling method for solving the electromagnetic inverse medium problem, Inverse Probl., 18 (2002), 891–906. https://doi.org/10.1088/0266-5611/18/3/323 doi: 10.1088/0266-5611/18/3/323
    [20] I. Harris, D.-L. Nguyen, Orthogonality sampling method for the electromagnetic inverse scattering problem, SIAM J. Sci. Comput., 42 (2020), B722–B737. https://doi.org/10.1137/19M129783X doi: 10.1137/19M129783X
    [21] M. Haynes, J. Stang, M. Moghaddam, Real-time microwave imaging of differential temperature for thermal therapy monitoring, IEEE Trans. Biomed. Eng., 61 (2014), 1787–1797. https://doi.org/10.1109/TBME.2014.2307072 doi: 10.1109/TBME.2014.2307072
    [22] K. Ito, B. Jin, J. Zou, A direct sampling method to an inverse medium scattering problem, Inverse Probl., 28 (2012), 025003. https://doi.org/10.1088/0266-5611/28/2/025003 doi: 10.1088/0266-5611/28/2/025003
    [23] K. Ito, B. Jin, J. Zou, A direct sampling method for inverse electromagnetic medium scattering, Inverse Probl., 29 (2013), 095018. https://doi.org/10.1088/0266-5611/29/9/095018 doi: 10.1088/0266-5611/29/9/095018
    [24] L. Jofre, A. Broquetas, J. Romeu, S. Blanch, A. P. Toda, X. Fabregas, et al., UWB tomographic radar imaging of penetrable and impenetrable objects, Proc. IEEE, 97 (2009), 451–464. https://doi.org/10.1109/JPROC.2008.2008854 doi: 10.1109/JPROC.2008.2008854
    [25] S. Kang, S. Chae, W.-K. Park, A study on the orthogonality sampling method corresponding to the observation directions configuration, Res. Phys., 33 (2022), 105108. https://doi.org/10.1016/j.rinp.2021.105108 doi: 10.1016/j.rinp.2021.105108
    [26] S. Kang, W.-K. Park, A novel study on the bifocusing method in two-dimensional inverse scattering problem, AIMS Mathematics, 8 (2023), 27080–27112. https://doi.org/10.3934/math.20231386 doi: 10.3934/math.20231386
    [27] S. Kang, W.-K. Park, S.-H. Son, A qualitative analysis of the bifocusing method for a real-time anomaly detection in microwave imaging, Comput. Math. Appl., 137 (2023), 93–101. https://doi.org/10.1016/j.camwa.2023.02.017 doi: 10.1016/j.camwa.2023.02.017
    [28] J.-Y. Kim, K.-J. Lee, B.-R. Kim, S.-I. Jeon, S.-H. Son, Numerical and experimental assessments of focused microwave thermotherapy system at 925MHz, ETRI J., 41 (2019), 850–862. https://doi.org/10.4218/etrij.2018-0088 doi: 10.4218/etrij.2018-0088
    [29] A. Kirsch, S. Ritter, A linear sampling method for inverse scattering from an open arc, Inverse Probl., 16 (2000), 89–105. https://doi.org/10.1088/0266-5611/16/1/308 doi: 10.1088/0266-5611/16/1/308
    [30] F. L. Louër, M.-L. Rapún, Topological sensitivity for solving inverse multiple scattering problems in 3D electromagnetism. Part Ⅰ: one step method, SIAM J. Imaging Sci., 10 (2017), 1291–1321. https://doi.org/10.1137/17M1113850 doi: 10.1137/17M1113850
    [31] J. J. Mallorqui, N. Joachimowicz, A. Broquetas, J. C. Bolomey, Quantitative images of large biological bodies in microwave tomography by using numerical and real data, Electron. Lett., 32 (1996), 2138–2140. https://doi.org/10.1049/el:19961409 doi: 10.1049/el:19961409
    [32] A. T. Mobashsher, A. M. Abbosh, On-site rapid diagnosis of intracranial hematoma using portable multi-slice microwave imaging system, Sci. Rep., 6 (2016), 37620. https://doi.org/10.1038/srep37620 doi: 10.1038/srep37620
    [33] J. R. Moreira, W. Keydel, A new MTI-SAR approach using the reflectivity displacement method, IEEE. Trans. Geosci. Remote, 33 (1995), 1238–1244. https://doi.org/10.1109/36.469488 doi: 10.1109/36.469488
    [34] G. Oliveri, N. Anselmi, A. Massa, Compressive sensing imaging of non-sparse 2D scatterers by a total-variation approach within the Born approximation, IEEE Trans. Antenn. Propag., 62 (2014), 5157–5170. https://doi.org/10.1109/TAP.2014.2344673 doi: 10.1109/TAP.2014.2344673
    [35] N. O. Önhon, M. Çetin, SAR moving object imaging using sparsity imposing priors, EURASIP J. Adv. Signal Process., 2017 (2017), 10. https://doi.org/10.1186/s13634-016-0442-z doi: 10.1186/s13634-016-0442-z
    [36] W.-K. Park, Asymptotic properties of MUSIC-type imaging in two-dimensional inverse scattering from thin electromagnetic inclusions, SIAM J. Appl. Math., 75 (2015), 209–228. https://doi.org/10.1137/140975176 doi: 10.1137/140975176
    [37] W.-K. Park, Multi-frequency subspace migration for imaging of perfectly conducting, arc-like cracks in full- and limited-view inverse scattering problems, J. Comput. Phys., 283 (2015), 52–80. https://doi.org/10.1016/j.jcp.2014.11.036 doi: 10.1016/j.jcp.2014.11.036
    [38] W.-K. Park, Performance analysis of multi-frequency topological derivative for reconstructing perfectly conducting cracks, J. Comput. Phys., 335 (2017), 865–884. https://doi.org/10.1016/j.jcp.2017.02.007 doi: 10.1016/j.jcp.2017.02.007
    [39] W.-K. Park, Direct sampling method for retrieving small perfectly conducting cracks, J. Comput. Phys., 373 (2018), 648–661. https://doi.org/10.1016/j.jcp.2018.07.014 doi: 10.1016/j.jcp.2018.07.014
    [40] W.-K. Park, Real-time microwave imaging of unknown anomalies via scattering matrix, Mech. Syst. Signal Proc., 118 (2019), 658–674. https://doi.org/10.1016/j.ymssp.2018.09.012 doi: 10.1016/j.ymssp.2018.09.012
    [41] W.-K. Park, Application of MUSIC algorithm in real-world microwave imaging of unknown anomalies from scattering matrix, Mech. Syst. Signal Proc., 153 (2021), 107501. https://doi.org/10.1016/j.ymssp.2020.107501 doi: 10.1016/j.ymssp.2020.107501
    [42] W.-K. Park, Real-time detection of small anomaly from limited-aperture measurements in real-world microwave imaging, Mech. Syst. Signal Proc., 171 (2022), 108937. https://doi.org/10.1016/j.ymssp.2022.108937 doi: 10.1016/j.ymssp.2022.108937
    [43] W.-K. Park, A novel study on the orthogonality sampling method in microwave imaging without background information, Appl. Math. Lett., 145 (2023), 108766. https://doi.org/10.1016/j.aml.2023.108766 doi: 10.1016/j.aml.2023.108766
    [44] W.-K. Park, On the application of orthogonality sampling method for object detection in microwave imaging, IEEE Trans. Antenn. Propag., 71 (2023), 934–946. https://doi.org/10.1109/TAP.2022.3220033 doi: 10.1109/TAP.2022.3220033
    [45] W.-K. Park, On the identification of small anomaly in microwave imaging without homogeneous background information, AIMS Mathematics, 8 (2023), 27210–27226. https://doi.org/10.3934/math.20231392 doi: 10.3934/math.20231392
    [46] W.-K. Park, H. P. Kim, K.-J. Lee, S.-H. Son, MUSIC algorithm for location searching of dielectric anomalies from ${S}-$parameters using microwave imaging, J. Comput. Phys., 348 (2017), 259–270. http://doi.org/10.1016/j.jcp.2017.07.035 doi: 10.1016/j.jcp.2017.07.035
    [47] W.-K. Park, D. Lesselier, Reconstruction of thin electromagnetic inclusions by a level set method, Inverse Probl., 25 (2009), 085010. https://doi.org/10.1088/0266-5611/25/8/085010 doi: 10.1088/0266-5611/25/8/085010
    [48] R. Potthast, A study on orthogonality sampling, Inverse Probl., 26 (2010), 074015. https://doi.org/10.1088/0266-5611/26/7/074015 doi: 10.1088/0266-5611/26/7/074015
    [49] Q. Rao, G. Xu, P. Wang, Z. Zheng, Study of the propagation characteristics of terahertz waves in a collisional and inhomogeneous dusty plasma with a ceramic substrate and oblique angle of incidence, Int. J. Antenn. Propag., 2021 (2021), 6625530. https://doi.org/10.1155/2021/6625530 doi: 10.1155/2021/6625530
    [50] T. Rubæk, P. M. Meaney, P. Meincke, K. D. Paulsen, Nonlinear microwave imaging for breast-cancer screening using Gauss–Newton's method and the CGLS inversion algorithm, IEEE Trans. Antenn. Propag., 55 (2007), 2320–2331. https://doi.org/10.1109/TAP.2007.901993 doi: 10.1109/TAP.2007.901993
    [51] M. Slaney, A. C. Kak, L. E. Larsen, Limitations of imaging with first-order diffraction tomography, IEEE Trans. Microwave Theory Tech., 32 (1984), 860–874. https://doi.org/10.1109/TMTT.1984.1132783 doi: 10.1109/TMTT.1984.1132783
    [52] S.-H. Son, K.-J. Lee, W.-K. Park, Application and analysis of direct sampling method in real-world microwave imaging, Appl. Math. Lett., 96 (2019), 47–53. https://doi.org/10.1016/j.aml.2019.04.016 doi: 10.1016/j.aml.2019.04.016
    [53] S.-H. Son, W.-K. Park, Application of the bifocusing method in microwave imaging without background information, J. Korean Soc. Ind. Appl. Math., 27 (2023), 109–122. https://doi.org/10.12941/jksiam.2023.27.109 doi: 10.12941/jksiam.2023.27.109
    [54] S.-H. Son, N. Simonov, H.-J. Kim, J.-M. Lee, S.-I. Jeon, Preclinical prototype development of a microwave tomography system for breast cancer detection, ETRI J., 32 (2010), 901–910. https://doi.org/10.4218/etrij.10.0109.0626 doi: 10.4218/etrij.10.0109.0626
    [55] W. Son, W.-K. Park, S.-H. Son, Microwave imaging method using neural networks for object localization, J. Electromagn. Eng. Sci., 22 (2022), 576–579. https://doi.org/10.26866/jees.2022.5.r.125 doi: 10.26866/jees.2022.5.r.125
    [56] A. E. Souvorov, A. E. Bulyshev, S. Y. Semenov, R. H. Svenson, A. G. Nazarov, Y. E. Sizov, et al., Microwave tomography: a two-dimensional Newton iterative scheme, IEEE Trans. Microwave Theory Tech., 46 (1998), 1654–1659. https://doi.org/10.1109/22.734548 doi: 10.1109/22.734548
    [57] I. Stojanovic, W. C. Karl, Imaging of moving targets with multi-static SAR using an overcomplete dictionary, IEEE J. Sel. Topics Signal Process., 4 (2010), 164–176. https://doi.org/10.1109/JSTSP.2009.2038982 doi: 10.1109/JSTSP.2009.2038982
    [58] G. Xu, Z. Song, Interaction of terahertz waves propagation in a homogeneous, magnetized, and collisional plasma slab, Wave. Random Media, 29 (2019), 665–677. https://doi.org/10.1080/17455030.2018.1462542 doi: 10.1080/17455030.2018.1462542
    [59] Y. Yin, W. Yin, P. Meng, H. Liu, On a hybrid approach for recovering multiple obstacles, Commun. Comput. Phys., 31 (2022), 869–892. https://doi.org/10.4208/cicp.OA-2021-0124 doi: 10.4208/cicp.OA-2021-0124
    [60] H. Yu, G. Xu, Z. Zheng, Transmission characteristics of terahertz waves propagation in magnetized plasma using the WKB method, Optik, 188 (2019), 244–250. https://doi.org/10.1016/j.ijleo.2019.05.061 doi: 10.1016/j.ijleo.2019.05.061
    [61] P. Zhang, P. Meng, W. Yin, H. Liu, A neural network method for time-dependent inverse source problem with limited-aperture data, J. Comput. Appl. Math., 421 (2023), 114842. https://doi.org/10.1016/j.cam.2022.114842 doi: 10.1016/j.cam.2022.114842
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(457) PDF downloads(55) Cited by(0)

Article outline

Figures and Tables

Figures(5)  /  Tables(1)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog