V.V. Razevig – Ph.D.(Eng.), Senior Research Scientist, 

Bauman Moscow State Technical University

E-mail: vrazevig@rslab.ru

A.I. Ivashov – Ph.D.(Eng.), Leading Engineer, 

Bauman Moscow State Technical University

Е-mail: ivashovai@mail.ru

A.S. Bugaev – Academic of RAS, Dr.Sc.(Phys.-Math.), Professor, Head of Department, 

Moscow Institute of Physics and Technology (State University)

Е-mail: bugaev@cplire.ru

A.V. Zhuravlev – Ph.D.(Phys.-Math.), Leading Research Scientist, 

Bauman Moscow State Technical University

E-mail: azhuravlev@rslab.ru

Microwave imaging is a technique for evaluation of hidden or embedded objects in an optically opaque structure (or media) using electromagnetic waves in microwave regime. Microwave imaging technique gives the electrical (i.e., electrical and magnetic property distribution) and geometrical (i.e., shape, size and location) parameters of an imaged object by solving a nonlinear inverse problem. In this paper, the focus is on short range microwave holographic imaging. Microwave holography has been used in a variety of applications such as: nondestructive testing and evaluation, concealed weapon detection at security check points, and medical imaging. These microwave holography technique operates by recording a microwave hologram which contains information about the magnitude and the phase of the wave reflected by a target. In modern microwave holography, scanned transceivers capture the scattered wavefront’s phase and amplitude distributions. Once recorded, the hologram can be reconstructed digitally using one among many hologram reconstruction methods.

In most cases, each researcher (or group of researchers) uses for solving his particular problem only one method chosen either according the advice of his colleagues or as a result of a literature review. And there are no guarantees, that the chosen method is the optimal method for particular conditions.

The aim of this paper is to compare different reconstruction methods such as back projection, Gauss–Newton and back propagation based on criteria such as reconstruction quality and computational complexity. The comparison is performed both on numerically simulated and experimentally registered microwave holograms.

Based on the results of numerical and physical experiments, the following conclusions can be made.

For microwave holograms recorded on a regular grid with a step not exceeding half the wavelength of the probing signal and at a low noise level (signal-to-noise ratio is greater than ~ 20 dB), all methods provide approximately the same quality of microwave images, while the speed of back propagation method, based on Fast Fourier Transform, two orders of magnitude higher than the other two methods.

With an increase of the noise level, Gauss-Newton method shows the best results, and the higher the noise level, the greater the advantage. Also, this method wins when processing microwave holograms registered on a regular grid with a step greater than half the wavelength, as well as on an irregular grid. In these cases, back projection method shows intermediate results, and back propagation method shows the worst ones.

In the case of sparse samples (both regular and irregular), the interpolation of the initial data on a regular grid with a small step can significantly improve the results of all methods.

A significant drawback of Gauss-Newton method is its high requirements to the RAM of the computer, as well as low speed of calculations. When processing microwave holograms with a large number of samples, calculations may require more memory than is installed in the computer, and the calculation time will increase many times due to the continuous exchange of data with the hard disk, or it will be impossible to do the calculations at all. In this case, back projection method can be a good alternative, which presents the lowest requirements for RAM, while showing somewhat worse, but quite acceptable results.

The results obtained will help new researchers who are going to use microwave imaging technology to solve their problems, to be oriented among the variety of existing methods and approaches.

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