On the Use of Electromagnetic Inversion for Near-Field Antenna Measurements: A Review

IEEE Trans. Antennas Propagat.

Vahabzadeh

et al. 2020

Self Cite

111

We show that the use of the electromagnetic inverse source framework offers great flexibility in the design of metasurfaces. In particular, this approach is advantageous for antenna design applications where the goal is often to satisfy a set of performance criteria such as half power beamwidths and null directions, rather than satisfying a fully-known complex field. In addition, the inverse source formulation allows the metasurface and the region over which the desired field specifications are provided to be of arbitrary shape. Some of the main challenges in solving this inverse source problem, such as formulating and optimizing a nonlinear cost functional, are addressed. Lastly, some two-dimensional (2D) and three-dimensional (3D) simulated examples are presented to demonstrate the method, followed by a discussion of the method's current limitations.

Section: B Electromagnetic Inversion For Chacterizationmentioning

confidence: 99%

“…This balancing idea has also been used in near-field antenna measurements[16] and microwave imaging applications[49].…”

mentioning

confidence: 99%

On the Use of Electromagnetic Inversion for Metasurface Design

Brown

IEEE Trans. Antennas Propagat.

Vahabzadeh

et al. 2020

Self Cite

111

“…In such applications, electromagnetic inverse scattering or inverse source algorithms are applied to measured data. For example, in antenna diagnostics [65,66], the near-field data of an antenna under test are measured. From this measured data, the equivalent currents of the antenna can be reconstructed using an electromagnetic inverse source algorithm, and can then, for example, be used for finding faulty elements in an antenna array.…”

Section: Electromagnetic Inversion For Characterizationmentioning

confidence: 99%

“…This balancing idea has also been used in near-field antenna measurements[66] and microwave imaging applications[97].…”

mentioning

confidence: 99%

Metasurface Design Using Electromagnetic Inversion

Brown

2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

Vahabzadeh

et al. 2019

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This thesis presents the theory and development of a framework for the design of electromagnetic metasurfaces. These metasurfaces can be used to systematically transform an incident electromagnetic field into a different transmitted field, providing new levels of control not typically possible with conventional materials. Although a metasurface is made up of subwavelength scattering elements, it can be macroscopically represented as a homogenized model using effective surface susceptibilities. These tensorial surface susceptibilities define the relationship between the tangential electric and magnetic fields on either side of the metasurface through a set of generalized boundary conditions known as the generalized sheet transition conditions.

“…These analytical expressions and the measured scattered field data are then compared to construct complex-valued calibration coefficients, which will then used to calibrate the actual measured data 5. The source reconstruction method is, in fact, an electromagnetic inverse source algorithm[43] 6. The L-curve method requires determining the knee point of the L-curve; in our implementation the knee point is chosen in an ad hoc manner.…”

mentioning

confidence: 99%

Phaseless Gauss-Newton Inversion for Microwave Imaging

IEEE Trans. Antennas Propagat.

Mojabi

2021

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A phaseless Gauss-Newton inversion (GNI) algorithm is developed for microwave imaging applications. In contrast to full-data microwave imaging inversion that uses complex (magnitude and phase) scattered field data, the proposed phaseless GNI algorithm inverts phaseless (magnitude-only) total field data. This phaseless Gauss-Newton inversion (PGNI) algorithm is augmented with three different forms of regularization, originally developed for complex GNI. First, we use the standard weighted L2 norm total variation multiplicative regularizer which is appropriate when there is no prior information about the object being imaged. We then use two other forms of regularization operators to incorporate prior information about the object being imaged into the PGNI algorithm. The first one, herein referred to as SL-PGNI, incorporates prior information about the expected relative complex permittivity values of the object of interest. The other, referred to as SP-PGNI, incorporates spatial priors (structural information) about the objects being imaged. The use of prior information aims to compensate for the lack of total field phase data. The PGNI, SL-PGNI, and SP-PGNI inversion algorithms are then tested against synthetic and experimental phaseless total field data. Index Terms-Microwave imaging, phaseless (magnitude-only) inversion, inverse scattering, Gauss-Newton inversion (GNI), and regularization.