This paper introduces a 3-D near-field microwave imaging approach, combining a special 2-D multiple-input multiple-output (MIMO) structure with orthogonal coding and Fourier domain processing. The proposed MIMO coded generalized reduced dimension Fourier algorithm effectively reduces data dimensionality while preserving valuable information, streamlining image reconstruction. Through mathematical derivations, we show how the proposed approach includes phase and amplitude compensators and reduces the computational complexity while mitigating propagation loss effects. Numerical simulations confirm the approach’s satisfactory performance in terms of information retrieval and processing speed.
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.
An approach to designing multiple waveforms in a multiple-input multiple-output (MIMO) system is presented so that the full capacity of the transmitting and receiving antennas can be utilized at the same time. On the transmitter-side, the antenna elements are classified into different groups according to their specific signal. On the receive-side, we use a multi-resolution analysis to retrieve the signals of each channel. Due to the superior characteristics of the FMCW signal, especially in terms of sampling, in the proposed approach, an FMCW radar is considered. To adapt the introduced system to multistatic near-field imaging, we use more accurate models than the effective phase center principle. This contributes to the successful reconstruction of the scene image by efficient Fourier-based image reconstruction methods. The performance of the proposed approach is confirmed by numerical simulations.
This paper proposes a simple design method for a multi-static aperiodic array to achieve 220 GHz sparse imaging, and a corresponding image reconstruction algorithm based on Fast Fourier Transform (FFT) and sparse data recovery. The proposed aperiodic sparse array originates from the linear sparse periodic array (SPA), it can further save the number of sampling data, transceivers and system cost compared to SPA imaging system. Low rank matrix recovery technique with principal component pursuit by alternating directions method (PCPADM) is used to recover the missing data caused by the sparse sampling. In order to achieve fast image reconstruction, FFT-based matched filtering method is used in which multistatic-to-monostatic conversion and interpolation are applied for data pre-processing. The proposed imaging scheme has been verified in experiments. An imaging resolution of 6 mm resolution is achieved at 1.4 m with 192 mm × 300 mm field of view, with a significantly reduced reconstruction time in comparison to the generalized synthetic aperture focusing technique (GSAFT).
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