This paper proposes a novel approach to 3-D microwave imaging using dynamic metasurface antennas in a multistatic configuration. By introducing a panel-to-panel model and a preprocessing technique, raw measurements are converted into the space-frequency domain for efficient data acquisition and reconstruction. Adapting the range migration algorithm in this work enables fast Fourier-based image reconstruction. Simulation results showcase the effectiveness of the proposed method, highlighting its potential for real-world applications.
In recent years, dynamic metasurface antennas (DMAs) have been proposed as an efficient alternative platform for computational imaging, which can drastically simplify the hardware architecture. In this paper, we first mathematically describe the existing solution to be able to convert raw measurements obtained by a DMA in the frequency-space domain into raw data on Fourier bases. Next, an optimization problem based on compressive sensing theory is defined, through which only a limited share of the total frequency/spatial data will be needed. The converted/retrieved data are used to reconstruct the image in the Fourier domain. The performance of the corresponding image reconstruction techniques (with/without Stolt interpolation operation) is evaluated in terms of the quality of the reconstructed image (both visually and quantitatively) and computational time with computer simulations.
In this paper, first, the structure of a linear sparse periodic array for two-dimensional scanning is described. Then, based on its characteristics, an algorithm is presented for fast image reconstruction of the scene in a near-field (NF) multistatic terahertz imaging scenario. Although the basis of this algorithm is developed in the Fourier domain, it is compatible with the non-uniform structure of the array and also takes into account the phase deviations caused by multistatic imaging in NF. The performance of the proposed approach is evaluated with numerical data obtained from electromagnetic simulations in FEKO as well as experimental data. The results are discussed in terms of computational time on the central processing unit and graphics processing unit as well as the quality of the reconstructed image.
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