Original articleResidual correction techniques for the efficient solution of inverse scattering problems
Introduction
We introduce the notation. Let , be the set of real numbers, and complex numbers, respectively. Let , be the N-dimensional real Euclidean space, and the N-dimensional complex Euclidean space, respectively. Let , , we denote with the Euclidean scalar product of and , the superscript ⊺ means transposed, and denotes the Euclidean norm of . Let . Let , we denote with the complex conjugate of z, with Re(z), Im(z) the real and the imaginary part of z respectively. We denote with ı the imaginary unit. We denote with the space of complex matrices having M rows and N columns.
We consider a two-dimensional inhomogeneous isotropic medium. The inhomogeneity of the medium is contained in a compact set . Let be the refractive index of the medium, so that for , and , for .
We consider an electromagnetic wave propagating on this medium. We suppose that this is a time-harmonic plane wave, so that the space-variables dependent part is given bywhere k > 0 is the wave number, is the propagation direction. The interaction of the inhomogeneity in the medium and the incident plane wave ui generates a scattered wave. This scattered wave is supposed harmonic in time and with the same time-frequency of the incident wave; we denote with , its space-variables dependent part.
Function is the solution of the following boundary-value problem:where is the Laplace operator with respect to the variables, is the gradient operator with respect to the variables, for , and m = 1 − n is the contrast function; note that, boundary condition (3) is the so called Sommerfeld radiation condition, see [6, p. 206] for a general presentation of this problem. Problems (2), (3) provides a reduced formulation of the electromagnetic scattering problem; this formulation holds for Transverse Magnetic (TM) waves, see [15, Chapter 6] for details. We can assume a TM symmetry when the refractive index of the obstacle and the incident field are independent of a coordinate variable; note that, for a plane wave this holds when it is linearly polarized, and has the electric field along to a coordinate axis, see [7], [12] for details on a practical situation where these assumptions hold. Here, to fix the ideas, we assume that this symmetry holds with respect to the x3 coordinate, so problem (2), (3) are given in terms of coordinates .
The solution us,n, of problem (2), (3), has the following asymptotic behaviour:where u0,n is the so called far field pattern, see [6, p.66] for details.
As in the classical scattering theory we can consider two different kinds of problems: the direct scattering problem and the inverse scattering problem. The direct scattering problem requires the computation of , , solution of boundary-value problem (2), (3), from the knowledge of k, , and of the refractive index n. The inverse scattering problem requires the computation of the refractive index , from some knowledge of the scattered wave us,n. In particular, several applications consider the scattered wave in the far field zone; this is a region, far from the obstacle, where the far field pattern provides an accurate approximation of the scattered wave. So, the following inverse problem is usually considered.
Problem 1 (Inverse scattering problem) Let be the set of the incidence directions; for j = 1, 2, …, J, let , be the set of the measurement directions associated to . From the knowledge of compute the refractive index .
Methods for the solution of Problem 1 are usually based on the integral formulation of boundary-value problem (2), (3). This formulation provides a pair of integral equations involving n, u0,n, and un = ui + us,n, that is the total field. These integral equations, due to the presence of un, are nonlinear integral equations for the unknown n. A simple approach to solve these equations is given by the Born approximation, where it is assumed that the total field un, in D, is well approximated by ui. This approximation is not well-accurate when the contrast function m and/or the diameter of D are large with respect to 1/k; in such situations the corresponding solution of Problem 1 is usually affected by large errors. Several different methods have been proposed to avoid this drawback of the Born approximation, such as for example the iterative Born approximation [16], the distorted Born approximation [5], [13], the modified gradient method [2], the Newton–Kantorovich method [14], and the dual space method [6, p. 266].
We study the numerical solution of Problem 1. The integral formulation of problem (2), (3) are used to obtain, through a suitable discretization scheme, a non-linear system. The solution of this system is given by the discretization variables of refractive index n. So, an approximate solution of Problem 1 can be computed from the least squares solution of such a non-linear system. Note that this can be seen as a general scheme to solve Problem 1. Different choices can be considered in the discretization of integral equations or in the numerical solution of the resulting non-linear system, but a time-consuming procedure is always obtained.
We propose the use of a residual correction technique for the efficient solution of Problem 1. In particular, this technique allows an efficient solution of the integral equation arising from problem (2), (3), and it can be profitably used in the solution of Problem 1. This technique is based on a linearization, with respect to function n, of the integral formulation of problem (2), (3). Note that, a similar linearization formula has been already used in a derivation of the Newton–Kantorovich method [13]. In the present paper, this technique is used together with the inversion method presented in [9], [8] in order to obtain an efficient numerical solution of Problem 1. In particular, the resulting method can be seen as an improvement of the method proposed in [9], [8].
In Section 2 we describe the integral formulation of problem (2), (3), and the numerical solution of Problem 1. In Section 3 we describe the residual correction technique and its use in the numerical solution of Problem 1. In Section 4 we show some numerical results obtained in the solution of Problem 1. Finally, in Section 5 we give conclusions and future possible developments of this paper.
Section snippets
The numerical solution of the inverse scattering problem
Boundary value problem (2), (3) has an equivalent formulation. Let un = ui + us,n be the total field. Function un is the solution of the Lippmann–Schwinger equation ([1, p. 364]), that is:where is the Hankel function of first kind and order 0. Note that function ui is a known function, so, from the solution un of (5), we can easily compute the solution us,n of problem (2), (3). From Eq. (5) we can obtain the integral
The residual correction technique
The solution of the inverse scattering problem requires to solve minimization problem (14), that is a time-consuming computation. We can consider a simple method to speed-up the solution of (14). In particular, for each j = 1, 2, …, J, vector is formally obtained from Eq. (11) and then it is substituted in Eq. (13), so, the corresponding least squares problem becomeswhere the minimum is computed with respect to . Note that
Numerical experiments
We present some results obtained in the numerical solution of Problem 1. We consider the following choice for the scattering data: wave number k = 210; incidence directions , θj = (π/18)j, j = 1, 2, …, 36 (J = 36); measurement directions for j th incident wave , l = 1, 2, …, 18 (L = 18). The numerical solution of Problem 1 is computed by solving minimization problem (15), and the discretization schemes (11), (13), are constructed by using s = 64. Integrals
Conclusions
We consider the electromagnetic scattering from an inhomogeneous medium. In particular, we consider the reconstruction of the refractive index of the medium from some knowledge of the scattered waves generated by the inhomogeneity and known incident waves. This problem is formulated by a system of two non-linear integral equations. So, its numerical solution can be obtained by suitable discretization schemes for these integral equations, and a minimization problem for the least squares solution
Acknowledgements
We thank C. Zhu, R.H. Byrd, P. Lu, J. Nocedal for making available free of charge the software package lbfgs_bcm.
We thank the Rome Laboratory, Electromagnetics & Reliability Directorate, 31 Grenier Street, Hanscom AFB, MA 01731-3010, USA, for the IPSWICH data.
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