Mathematical Problems in Engineering

Volume 2016, Article ID 7872548, 12 pages

http://dx.doi.org/10.1155/2016/7872548

## Interpretation of MUSIC for Location Detecting of Small Inhomogeneities Surrounded by Random Scatterers

Department of Mathematics, Kookmin University, Seoul 02707, Republic of Korea

Received 27 October 2015; Revised 5 January 2016; Accepted 6 January 2016

Academic Editor: Eric Florentin

Copyright © 2016 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

We consider the MUltiple SIgnal Classification (MUSIC) algorithm for identifying the locations of small electromagnetic inhomogeneities surrounded by random scatterers. For this purpose, we rigorously analyze the structure of MUSIC-type imaging function by establishing a relationship with zero-order Bessel function of the first kind. This relationship shows certain properties of the MUSIC algorithm, explains some unexplained phenomena, and provides a method for improvements.

#### 1. Introduction

One of the purposes of the inverse scattering problem is to identify the characteristics (location, shape, material properties, etc.) of small inhomogeneities from the scattered field or far-field pattern. This problem, which arises in fields such as physics, engineering, and biomedical science, is highly relevant to human life; thus, it remains an important research area. Related works can be found in [1–5] and references therein.

Attempts to address the problem described above have led to the development of the MUltiple SIgnal Classification- (MUSIC-) type algorithm to find unknown inhomogeneities and the algorithm has been applied to various problems, for example, detection of small inhomogeneities in homogeneous space [6–9], location identification of small inhomogeneities embedded in a half-space or multilayered medium [10–12], reconstructing perfectly conducting cracks [13, 14], imaging of internal corrosion [15], shape recognition of crack-like thin inhomogeneities [16–18] and volumetric extended targets [19–21], and application to the biomedical imaging [22]. We also refer to [23, 24] for a detailed and concise description of MUSIC. Several research efforts have contributed to confirming that MUSIC is a fast and stable algorithm that can easily be extended to multiple inhomogeneities and that does not require specific regularization terms that are highly dependent on the problem at hand. However, its feasibility is only confirmed when the background medium is homogeneous; that is, the imaging performance of MUSIC when unknown inhomogeneities are surrounded by random scatterers remains unknown. In several works [25–28], an inverse scattering problem in random media has been concerned. Specially, mathematical theory of MUSIC for detecting point-like scatterers embedded in an inhomogeneous medium has been concerned in [29]. Motivated by these remarkable works, a more careful investigation of the mathematical theory is still required.

Motivated by the above, MUSIC algorithm has been applied for detecting the locations of small electromagnetic inhomogeneities when they are surrounded by electromagnetic random scatterers and confirmed that it can be applied satisfactorily. However, this only relied on the results of numerical simulations, that is, a heuristic approach to some extent, which is the motivation for the current work. In this contribution, we carefully analyze the mathematical structure of MUSIC-type imaging function and discover some properties. This work is based on the relationship between the singular vectors associated with nonzero singular values of a multistatic response (MSR) matrix and asymptotic expansion formula due to the existence of small inhomogeneities; refer to [23].

This paper is organized as follows. Section 2 introduces the two-dimensional direct scattering problem and an asymptotic expansion formula in the presence of small inhomogeneities. In Section 3, MUSIC-type imaging function is introduced. In Section 4, we analyze the mathematical structure of the MUSIC-type imaging function and discuss its properties. In Section 5, we present the results of numerical simulations to support the analyzed structure of MUSIC and Section 6 presents a short conclusion.

#### 2. Two-Dimensional Direct Scattering Problem

In this section, we survey a two-dimensional direct scattering problem and introduce an asymptotic expansion formula. For a more detailed description we recommend [18, 23, 30]. Let , , be an electromagnetic inhomogeneity with a small diameter in two-dimensional space . Throughout this paper, we assume that every is expressed aswhere denotes the location of and is a simple connected smooth domain containing the origin. For the sake of simplicity, we let be the collection of . Throughout this paper, we assume that inhomogeneities are well separated from each other such thatfor all and .

Let us denote , , as the random scatterer with small radius and let be the collection of . Similarly, we assume that is of the formAs before, suppose that for all and and the positions of are random but they are fixed for all frequencies discussed later.

In this work, we assume that every inhomogeneity is characterized by its dielectric permittivity and magnetic permeability at a given positive angular frequency , where denotes the wavelength. Let , , and be the electric permittivities of , , and , respectively. Then, we can introduce the piecewise-constant electric permittivity and magnetic permeability such thatrespectively. For the sake of simplicity, we let , , and for all and . Hence, we can set the wavenumber .

For a given fixed frequency , we denoteto be a plane-wave incident field with the incident direction , where denotes the two-dimensional unit circle. Let denote the time-harmonic total field that satisfies the following Helmholtz equationwith transmission conditions on the boundaries of and . This configuration is associated with a scalar scattering problem for -polarized (Transverse Magnetic (TM) polarization, corresponding to dielectric contrasts) field; the -polarized (Transverse Electric (TE) polarization, corresponding to magnetic contrasts) case could be dealt with per duality. It is well known that can be decomposed aswhere denotes the unknown scattered field that satisfies the Sommerfeld radiation conditionuniformly in all directions . The far-field pattern of the scattered field is defined on . It can be expressed asThen by virtue of [31], the far-field pattern can be written as the following asymptotic expansion formula, which plays a key role in the MUSIC-type algorithm that will be designed in the next section:

#### 3. MUSIC-Type Imaging Algorithm

In this section, we introduce the MUSIC-type algorithm for detecting the locations of small inhomogeneities. For the sake of simplicity, we exclude the constant term from (10). For this, let us consider the eigenvalue structure of the MSR matrixSuppose that for all ; then is a complex symmetric matrix but not a Hermitian. Thus, instead of eigenvalue decomposition, we perform singular value decomposition (SVD) of (see [24], for instance)where superscript is the mark of a Hermitian. Then, is the orthogonal basis for the signal space of . Therefore, one can define the projection operator onto the null (or noise) subspace, . This projection is given explicitly bywhere denotes the identity matrix. For any point and suitable vectors , , define a test vector asThen, by virtue of [23], there exists such that, for any , the following statement holds:for and . This means that if or then . Thus, the locations of and follow from computing the MUSIC-type imaging functionThe resulting plot of will have peaks of large magnitudes at and .

*Remark 1. *Based on several works [17, 18, 20], selection of in (14) is highly depending on the shape of . Unfortunately, the shape of is unknown; it is impossible to find proper vectors . Due to this fact, following [20], we assume that for all ; that is, we consider the following test vector instead of (14):and we analyze the mathematical structure of .

#### 4. Structure of Imaging Function

Henceforth, we analyze the mathematical structure of and examine certain of its properties. Before starting, we recall a useful result derived in [32].

Lemma 2. *Assume that spans . Then, for sufficiently large , , and , the following relation holds:where denotes Bessel function of order of the first kind.*

Now, we introduce the main result.

Theorem 3. *For sufficiently large and , can be represented as follows: for and , *

*Proof. *Based on the asymptotic expansion formula (10) and results in [13], can be represented aswhereWith this, applying (18) and performing a tedious calculation, we arrive atwherefor and . By implementing elementary calculus, we can show thatwhereFirst, applying (18), we can obtainThis leads us toand similarly toNext, based on the orthonormal property of singular vectors, relations (2) and (18), and the following asymptotic formwe can deriveand similarlyFor evaluating , let us perform an elementary calculus Then, we can conclude thatFinally, for , by applying following integral, for ,we can derive the following:Correspondingly,Hence, by combining (27)–(36), we can obtain the following mathematical structure: This enables us to obtain the desired result. This completes the proof.

*Remark 4 (applicability of MUSIC). *Since , the value of will be sufficiently large when or for all and . Hence, based on the result in Theorem 3, the locations of and can be identified via the map of . This is the reason why it is possible to detect the locations of small inhomogeneities as well as random scatterers. Note that, for a successful detection, based on the hypothesis in Theorem 3, the value of (at least, greater than ) and must be sufficiently large enough. If applied frequency is low or total number of is small, poor result would appear in the map of .

*Remark 5 (discrimination of singular values). *Theoretically, if the size, permittivity, and permeability of the random scatterers are smaller than those of the inhomogeneities, then for all and . This means that if it were possible to discriminate singular values associated with small inhomogeneities, then the structure of would becomeHence, it is expected that more good results can be obtained. Our approach presents an improvement. However, if the relation were no longer valid, the locations of random scatterers would have to be identified via MUSIC such that poor results would appear in the map of .

#### 5. Results of Numerical Simulations

Selected results of numerical simulations are presented here to support the identified structure of the MUSIC-type imaging function. In this section, we only consider the dielectric permittivity contrast case; that is, we set , , and for all and . The radius of all and is set to and , respectively. The applied angular frequency is and a total of number of incident directions is applied such that

small inhomogeneities are selected with locations , , and . We set number of small scatterers as being randomly distributed in such thatfor all and also select the permittivities randomly aswhere , , and , is an arbitrary real value within . Refer to Figure 1 for a sketch of the distribution of the three inhomogeneities and random scatterers.