Advances in Mathematical Physics
Volume 2018 (2018), Article ID 2096058, 8 pages
https://doi.org/10.1155/2018/2096058

Research Article

Topological Derivative for Imaging of Thin Electromagnetic Inhomogeneity: Least Condition of Incident Directions

Won-Kwang Park

Department of Information Security, Cryptology, and Mathematics, Kookmin University, Seoul, Republic of Korea

Correspondence should be addressed to Won-Kwang Park; parkwk@kookmin.ac.kr

Received 19 June 2017; Accepted 28 January 2018; Published 22 February 2018

Academic Editor: Antonio Scarfone

Copyright © 2018 Won-Kwang Park. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

It is well-known that using topological derivative is an effective noniterative technique for imaging of crack-like electromagnetic inhomogeneity with small thickness when small number of incident directions are applied. However, there is no theoretical investigation about the configuration of the range of incident directions. In this paper, we carefully explore the mathematical structure of topological derivative imaging functional by establishing a relationship with an infinite series of Bessel functions of integer order of the first kind. Based on this, we identify the condition of the range of incident directions and it is highly depending on the shape of unknown defect. Results of numerical simulations with noisy data support our identification.

1. Introduction

In this paper, we consider topological derivative [1] based imaging technique for thin, curve-like penetrable electromagnetic inhomogeneity with small thickness. Originally, this has been considered for shape optimization problems [2–7] and was then successfully combined with the level-set method (see [8–13]) for various inverse scattering problems. Surprisingly, throughout some researches [14, 15], it has been confirmed that topological derivative is also one of noniterative imaging techniques and very effective algorithm. However, as we can see [15–21], the most work has considered full-aperture problems.

Throughout results of numerical simulations, it has been confirmed that topological derivatives can be applied in limited-aperture problems, and an analysis in limited-aperture problems has been performed in [22]. In this interesting research, a relationship between topological derivative imaging function and an infinite series of Bessel functions of first kind has been established and, correspondingly, a sufficient condition of the range of incident directions for application has been identified theoretically. However, a least condition of application still remains unknown. Motivated by this fact, we identify a least condition of the range of incident directions for a successful application in limited-aperture inverse scattering problem and confirm this condition is highly depending on the unknown shape of thin inhomogeneity.

The remainder of paper is organized as follows. In Section 2, we survey two-dimensional direct scattering problem and topological derivative based imaging technique. In Section 3, we investigate a least condition of the range of incident directions and discuss its properties. In Section 4, several results of numerical simulations with noisy data are presented in order to support our investigation. A brief conclusion is given in Section 5.

2. Introduction to Direct Scattering Problem and Topological Derivative

Let be a homogeneous domain with smooth boundary that contains a homogeneous thin inhomogeneity with a small thickness . That is,

where is the unit normal to at and denotes the a simple, smooth curve in which describes the supporting curve of . In this contribution, we assume that the applied angular frequency is of the form . We assume that all materials are characterized by their dielectric permittivity and magnetic permeability at frequency of operation ; we define the piecewise constant permittivity and permeability as

respectively. For this sake, we set and denote as the wavenumber, where is a given wavelength and satisfies .

Let be the time-harmonic total field that satisfies Helmholtz equation

with boundary condition

and with transmission conditions on the boundary of . Here, denotes a two-dimensional vector on the connected, proper subset of unit circle such that

Similarly, let be the background solution of (3) with boundary condition (4).

Now, we introduce the basic concept of topological derivative operated at a fixed single frequency. The problem considered herein is the minimization of the tracking type functional depending on the solution :

Assume that an electromagnetic inclusion of small diameter is created at a certain position , and let denote this domain. Since the topology of the entire domain has changed, we can consider the corresponding topological derivative based on with respect to point as

where as . From (7), we can obtain an asymptotic expansion:

In [21], the following normalized topological derivative imaging function has been introduced:

Here, and satisfying (8) for purely dielectric permittivity contrast ( and ) and magnetic permeability contrast ( and ) cases, respectively, are explicitly expressed as (see [21])

where satisfies the adjoint problem

3. Least Condition of Incident Directions

In this section, we identify the least condition of incident directions for applying topological derivative. For this, we introduce the structure of and as follows.

Lemma 1 (see [20, 21]). Suppose that and are sufficiently large; then

where and are unit vectors that are, respectively, tangent and normal to the supporting curve at , and

Based on Lemma 1, the structure of (9) in limited-aperture problem can be represented as follows. This result plays an important role in identifying least condition of incident directions. For a detailed proof, we refer to [22].

Theorem 2. Let and . If and are sufficiently large, the structure of (9) becomes

where

with

and the term does not contribute to the imaging performance.

In recent work [20], it has been confirmed that the application of multifrequencies guarantees better a imaging performance than the application of a single frequency. Therefore, we consider the following normalized multifrequency topological derivative: for several frequencies , define

Based on the structure of in Theorem 2, we can observe that the terms and contribute to and disturb the imaging performance, respectively, for . Hence, eliminating the term will guarantee a good result. This means that the least condition comes from

for all and . Note that since satisfies the asymptotic property

for , good results can appear in the map of when . Unfortunately, this is an ideal condition. Furthermore, since is arbitrary, we cannot control the value of the term . So, we must find a condition of and :

for all . A simple way is to select and such that

that is, for any , a selection and will guarantee good results via topological derivative. Based on this, we can obtain the following theoretical result of the range of incident directions, which has been examined heuristically.

Theorem 3 (least condition of range). The least range of incident directions for successful application of topological derivative is highly depending on the shape of unknown thin inhomogeneity and the range of directions must be wider than .

4. Simulations Results and Discussions

In this section, some results of numerical simulations are exhibited to support identified condition mentioned in previous section. The homogeneous domain is chosen as a unit circle centered at the origin, and two supporting curves of the are selected as

The thickness of all is set to , and parameters , are chosen as 1. Let and for denote the permittivity and permeability of , respectively. The applied frequency is selected as , with , , and different incident directions chosen. In order to show the robustness, a white Gaussian noise with 20 dB signal-to-noise ratio (SNR) was added to the unperturbed boundary data.

First, let us consider the imaging of straight line shaped thin inhomogeneity . In this case, since , when , and will be a good choice for any . Corresponding results for , , , and are exhibited in Figure 1. Based on these results, we can observe that, for any value of , the shape of was retrieved satisfactorily via with the range of incident directions. However, although one can recognize the existence of inhomogeneity, the shape of cannot be reconstructed satisfactorily when .

(a) and

(b) and

(c) and

(d) and

Figure 1: Maps of for . Violet-colored solid line describes the range of incident directions.

Based on the results in Figure 1, we can conclude that one can identify the shape of existence of for any choice of . But if the values of and are satisfying

the result via the map of is very poor. This is the worst choice of the selection. In contrast, if the values of and are satisfying

an acceptable result should be obtained via the map of ; refer to Figure 2. With this observation, we can conclude that if and are satisfying

a good result of can be obtained; refer to Figure 3.

Figure 2: Description of best (blue-colored range) and worst (red-colored range) choice of the range of incident directions.

(a) and

(b) and

Figure 3: Same as Figure 1 except the range of the incident directions.

Now, let us consider the influence of range of incident directions when the shape of thin inhomogeneity is no more straight line. For this purpose, we choose thin inclusion and compare maps of for various range of incident directions. Figure 4 shows maps of for

where , , , and . Based on these result, we can observe that if the range of directions is narrow, we cannot recognize the existence of ; refer to Figures 4(a) and 4(b). Note that when the range of directions satisfies the sufficient condition in [22], the shape of can be identified; refer to Figure 4(d). However, if one selects the optimal range, adopted for imaging of , the result is still poor (see Figure 4(c)).

(a) and

(b) and

(c) and

(d) and

Figure 4: Maps of for . Violet-colored solid line describes the range of incident directions.

In order to find the least condition, let us reconsider the imaging of . In this case, the selection of (24) was a good choice. Following this observation, one of the possible choices of is that, for , , select

Then, it is expected that identified shape of inhomogeneity will be close to the shape of . Notice that, throughout the numerical computation,

Thus, selection of (27) is the least condition of the range of incident directions. Figure 6 exhibits maps of for and in (27) and for and (see Figure 5 for instance). Since these conditions satisfy least condition, the shape of seems retrieved well, and this result supports Theorem 3.

Figure 5: Description of best choice of the range of incident directions.

(a) and satisfy (27)

(b) and

Figure 6: Map of for when inhomogeneity is .

For the final example, let us consider the imaging of two, nonoverlapped thin inhomogeneities and , where the supporting curve of is

and . Based on the results in Figure 7, we can conclude that it is hard to recognize the shape of when the range , but we can identify when . With this, we end up this section with the following remark.

(a) and

(b) and

(c) and

(d) and

Figure 7: Maps of for . Violet-colored solid line describes the range of incident directions.

Remark 4 (condition for imaging of multiple inhomogeneities). Due to the shape dependency of the range of directions, when the shapes of thin inhomogeneities are not straight line, the range of directions must be close to , which is the sufficient condition of range of application. Related results of numerical simulations can be found in [22] also.

5. Conclusion

In this paper, we have considered the topological derivative in a limited-aperture inverse scattering problem for a noniterative imaging of thin inhomogeneity. Based on the relationship between topological derivative imaging function and infinite series of Bessel functions of integer order of the first kind, we discovered a least condition of the range of incident directions for successful application. We presented the results of some numerical simulations, which show that the discovered condition is valid for the imaging of a thin inclusion. Here, we have considered an imaging of thin penetrable inhomogeneity but the analysis could be carried out for a perfectly conducting crack. Furthermore, the extension to inverse elasticity problems will be an interesting subject. Finally, extension to the three-dimensional [23, 24] and real-world problem [25, 26] will be a remarkable research topic.

Conflicts of Interest

The author declares that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (no. NRF-2017R1D1A1A09000547) and the research program of Kookmin University in Korea.

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