MUSIC型成像在无背景信息异常检测中的应用
Application of MUSIC-type imaging for anomaly detection without background information
AI总结：
本文针对背景信息不完整时MUSIC算法定位小异常体失效的问题，推导了成像函数的解析表达式，证明识别受介电常数和电导率影响，但电导率较小时仍可识别，并用仿真验证。
摘要：
已有研究表明，多信号分类（MUSIC）算法在微波成像中定位小异常体方面具有快速、稳定和有效的特点。为了成功应用MUSIC算法，必须已知背景的介电常数、电导率和磁导率的精确值。如果这些值中有一个未知，算法将无法识别异常体的位置。然而，据我们所知，目前尚未有研究对这一失败原因给出解释。本文考虑了在背景信息不完整的情况下，利用散射参数数据应用MUSIC算法定位小异常体的问题。借助散射参数数据的积分方程表述框架，推导了MUSIC型成像函数的解析表达式，该表达式以整数阶贝塞尔函数的无穷级数形式给出。基于理论结果，我们确认小异常体的识别受到所应用的介电常数和电导率值的显著影响。然而，幸运的是，当所应用的电导率值较小时，仍有可能识别异常体。报告了使用合成数据的仿真结果，以验证理论结果。
英文摘要：
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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