应用 MUSIC 型成像技术进行无背景信息的异常检测

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Won-Kwang Park
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引用次数: 0

摘要

研究表明,多重信号分类(MUSIC)算法快速、稳定,并能有效定位微波成像中的微小异常。要成功应用 MUSIC,必须知道背景的介电常数、电导率和磁导率的精确值。如果其中一个值未知,就无法确定异常点的位置。然而,据我们所知,目前还没有人对这种失败做出解释。在本文中,我们将考虑在没有完整背景信息的情况下,将 MUSIC 应用于从散射参数数据中定位一个小的异常点。借助散射参数数据积分方程公式框架,我们得出了 MUSIC 型成像函数在整数阶贝塞尔函数无穷序列方面的解析表达式。根据这一理论结果,我们证实了小异常的识别会受到所应用的介电常数和电导率值的显著影响。不过,幸运的是,如果应用的电导率值较小,就有可能识别出异常点。为了证明理论结果,我们报告了合成数据的模拟结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of MUSIC-type imaging for anomaly detection without background information

It has been demonstrated that the MUltiple SIgnal Classification (MUSIC) algorithm is fast, stable, and effective for localizing small anomalies in microwave imaging. For the successful application of MUSIC, exact values of permittivity, conductivity, and permeability of the background must be known. If one of these values is unknown, it will fail to identify the location of an anomaly. However, to the best of our knowledge, no explanation of this failure has been provided yet. In this paper, we consider the application of MUSIC to the localization of a small anomaly from scattering parameter data when complete information of the background is not available. Thanks to the framework of the integral equation formulation for the scattering parameter data, an analytical expression of the MUSIC-type imaging function in terms of the infinite series of Bessel functions of integer order is derived. Based on the theoretical result, we confirm that the identification of a small anomaly is significantly affected by the applied values of permittivity and conductivity. However, fortunately, it is possible to recognize the anomaly if the applied value of conductivity is small. Simulation results with synthetic data are reported to demonstrate the theoretical result.

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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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