Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-20T17:36:17.406Z Has data issue: false hasContentIssue false

Scattering coefficients of inhomogeneous objects and their application in target classification in wave imaging

Published online by Cambridge University Press:  03 July 2019

LORENZO BALDASSARI*
Affiliation:
Department of Mathematics, ETH Zürich, Rämistrasse 101, CH-8093 Zürich, Switzerland e-mail: [email protected]

Abstract

The aim of this paper is to provide and numerically test in the presence of measurement noise a procedure for target classification in wave imaging based on comparing frequency-dependent distribution descriptors with precomputed ones in a dictionary of learned distributions. Distribution descriptors for inhomogeneous objects are obtained from the scattering coefficients. First, we extract the scattering coefficients of the (inhomogeneous) target from the perturbation of the reflected waves. Then, for a collection of inhomogeneous targets, we build a frequency-dependent dictionary of distribution descriptors and use a matching algorithm in order to identify a target from the dictionary up to some translation, rotation and scaling.

Type
Papers
Copyright
© Cambridge University Press 2019

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

Abbas, T., Ammari, H., Hu, G., Wahab, A. & Ye, J.Ch. (2017) Elastic scattering coefficients and enhancement of nearly elastic cloaking. J. Elast. 128, 203243.CrossRefGoogle Scholar
Abramowitz, M. & Stegun, I. (1964) Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, National Bureau of Standards Applied Mathematics Series, Vol. 55, Dover, New York.Google Scholar
Ammari, H., Boulier, T. & Garnier, J. (2014) Shape recognition and classification in electro-sensing. Proceedings of the National Academy of Sciences USA 111, 1165211657.CrossRefGoogle ScholarPubMed
Ammari, H., Boulier, T., Garnier, J., Jing, W., Kang, H. & Wang, H. (2014) Target identification using dictionary matching of generalized polarization tensors. Found. Comput. Math. 14, 2762.CrossRefGoogle Scholar
Ammari, H., Chow, Y. T. & Zou, J. (2014) The concept of heterogeneous scattering coefficients and its application in inverse medium scattering. SIAM J. Math. Anal. 46, 29052935.CrossRefGoogle Scholar
Ammari, H., Chow, Y. T. & Zou, J. (2014) The concept of heterogeneous scattering coefficients and its application in inverse medium scattering, preprint, arXiv:1310.6096, 2013.Google Scholar
Ammari, H., Chow, Y. T. & Zou, J. (2016) Phased and phaseless domain reconstruction in inverse scattering problem via scattering coefficients. SIAM J. Appl. Math. 76, 10001030.CrossRefGoogle Scholar
Ammari, H., Chow, Y. T. & Zou, J. (2018) Super-resolution in imaging high contrast targets from the perspective of scattering coefficients. J. Math. Pures Appl. 111, 191226.CrossRefGoogle Scholar
Ammari, H., Deng, Y., Kang, H. & Lee, H. (2014) Reconstruction of inhomogeneous conductivities via generalized polarization tensors. Ann. IHP Anal. Non Lin. 31, 877897.Google Scholar
Ammari, H., Garnier, J., Jing, W., Kang, H., Lim, M., Sølna, M. & Wang, G. (2013) Mathematical and Statistical Methods for Multistatic Imaging, Springer, Cham, Switzerland.CrossRefGoogle Scholar
Ammari, H. & Kang, H. (2004) Boundary layer techniques for solving the Helmholtz equation in the presence of small inhomogeneities. J. Math. Anal. Appl. 296, 190208.CrossRefGoogle Scholar
Ammari, H., Kang& H., Lee (2009) Layer potential techniques in spectral analysis, Mathematical Surveys and Monographs, vol. 153, American Mathematical Society, Providence, RI. MR2488135CrossRefGoogle Scholar
Ammari, H., Kang, H., Lee, H. & Lim, M. (2013) Enhancement of Near-Cloaking. Part II: The Helmholtz Equation. Comm. Math. Phys. 317, 485502.CrossRefGoogle Scholar
Ammari, H., Garnier, J., Jugnon, V., Lee, H. & Lim, M. (2012) Enhancement of near-cloaking. Part III: Numerical simulations, statistical stability, and related questions. In: Multi-scale and High-Contrast PDE: from Modelling, to Mathematical Analysis, to Inversion, Contemp. Math., Vol. 577, Amer. Math. Soc., Providence, RI, pp. 124.CrossRefGoogle Scholar
Ammari, H., Tran, M. P. & Wang, H. (2014) Shape identification and classification in echolocation. SIAM J. Imaging Sci. 7, 18831905.CrossRefGoogle Scholar
Ammari, H., Kang, H., Kim, E. & Lee, J.-Y. (2012) The generalized polarization tensors for resolved imaging. Part II: Shape and electromagnetic parameters reconstruction of an electromagnetic inclusion from multistatic measurements. Math. Comp. 81, 839860.CrossRefGoogle Scholar
Ammari, H., Fitzpatrick, B., Kang, H., Ruiz, M., Yu, S. & Zhang, H. (2018) Mathematical and computational methods in photonics and phononics, Mathematical Surveys and Monographs, Vol. 235, Amer, Math. Soc., Providence.CrossRefGoogle Scholar
Colton, D. & Kress, R. (1992) Inverse Acoustic and Electromagnetic Scattering Theory. Applied Math. Sciences, Vol. 93, Springer-Verlag, New York.CrossRefGoogle Scholar
Kleeman, L. & Kuc, R. (1995) Mobile robot sonar for target localization and classification. Internat. J. Robotics Res. 14, 295318.CrossRefGoogle Scholar
Nédélec, J. C. (1992) Quelques propriétés des dérivées logarithmiques des fonctions de Hankel. C. R. Acad. Sci. Paris, Série I 314, 507510.Google Scholar
Pólya, G. & Szegö, G. (1951) Isoperimetric Inequalities in Mathematical Physics, Ann. Math. Stud. Vol. 27, Princeton University Press, Princeton, NJ.Google Scholar
Simmons, J. A. (1979) Perception of echo phase information in bat sonar. Science 204, 13361338.CrossRefGoogle ScholarPubMed
Simmons, J. A., Fenton, M. B. & O’Farrell, M. J. (1979) Echolocation and pursuit of prey by bats. Science 203, 1621.CrossRefGoogle ScholarPubMed
Wang, H. (2014) Shape identification in electro-sensing, https://github.com/yanncalec/SIESGoogle Scholar