Two Dimensional Array Imaging With Beam Steered Data

Patole, Sujeet; Torlak, Murat

doi:10.1109/tip.2013.2282115

Cited by 24 publications

(9 citation statements)

References 14 publications

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“…It is observed that the images reconstructed by (9) have a better quality than those reconstructed by employing the matched filtering technique. The reason for this is that the matched filtering technique, despite its more straightforward implementation, has limitations in terms of resolution [50].…”

Section: Simulation Resultsmentioning

confidence: 99%

Fourier Compatible Near-Field Multiple-Input Multiple-Output Terahertz Imaging With Sparse Non-Uniform Apertures

Molaei¹,

Skouroliakou

et al. 2021

IEEE Access

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Section: Simulation Resultsmentioning

confidence: 99%

Fourier Compatible Near-Field Multiple-Input Multiple-Output Terahertz Imaging With Sparse Non-Uniform Apertures

Molaei¹,

Skouroliakou

et al. 2021

IEEE Access

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“…In this section, both the simulated and real-measured experiments are performed to validate the effectiveness and efficiency of the proposed method. The comparison baselines include two types of imaging algorithms, i.e., the conventional MF-based imaging methods, including range migration algorithm (RMA), MF in frequency domain [47], and SF holography algorithm (SF Holo) [42]; the network-based reconstruction method, RMIST-Net [43]. Considering the superiority of RMIST-Net over ISTA [13] and CSR-Net [48] has been proven in [43], thus the comparisons with them are not provided here.…”

Section: Methodsmentioning

confidence: 99%

Lightweight FISTA-Inspired Sparse Reconstruction Network for mmW 3-D Holography

Wang

Wei

Liang

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Integrating compressed sensing (CS) with millimeter-wave (mmW) holography has shown great potential to achieve lightweight onboard hardware, low sampling ratio, and high-speed sensing. However, conventional CS-driven algorithms are always limited by nontrivial adjusting of parameters and excessive computational cost caused by plenty of iterations. To address this problem, we propose a lightweight model-based deep learning framework (LFIST-Net) for mmW 3-D holography, by combining the interpretability of fast iterative shrinkage-thresholding algorithm (FISTA) and tuning-free merit of data-driven deep neural network. First, the single-frequency (SF) holographic imaging technique is integrated into FISTA, which serves as the sensing kernels, to avoid large-scale matrix multiplications. Subsequently, the kernel-based FISTA (KFISTA) is mapped into layer-fixed and parameter-learnable LFIST-Net, whose weights are relaxed to be layer-varied. The updating of key parameters in LFIST-Net, including step sizes, thresholds, and momentum coefficients, are regularized by soft-plus function to ensure the non-negativity and monotonicity. As for 3-D holography implementation, the "1-D + 2-D" scheme is adopted, where the matched filtering (MF) and well-trained LFIST-Net are used for range focusing and reconstructions of azimuth slices. Without losing efficiency, the range-focused subechoes are processed parallelly in 3-D cube form. Experiments, including both simulated and measured tests based on a commercial mmW radar, prove that LFIST-Net is capable of reconstructing the imaging scene precisely. In particular, in near-field mmW 3-D holography tests, both numerical and visual results demonstrate LFIST-Net yields compelling reconstruction performance while maintaining high computational speed compared with MF-based, conventional CS-driven, and network-based methods. Index Terms-3-D holography, compressed sensing (CS), deep unfolding, fast iterative shrinkage-thresholding algorithm (FISTA), millimeter-wave (mmW) imaging, near-field, sparse reconstruction. I. INTRODUCTIONN EAR-FIELD 3-D millimeter-wave (mmW) holography has gained significant concerns in the microwave theory and techniques society. mmW, possessing the merits of reasonable penetration for most nonmetal materials and no known Manuscript

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“…In a more straightforward way, the 2D target reflectivity can also be obtained using the matched filtering technique [18] as follows:…”

Section: Image Reconstructionmentioning

confidence: 99%

“…where   , f z h x y is the impulse response evaluated at the focusing distance f z instead of 0 z . For a point target located at the origin of the target coordinate system   0 0, 0, z , the received reflected wave data is given by [18,19]  …”

Section: Image Reconstructionmentioning

confidence: 99%

Fast Processing Approach for Near-Field Terahertz Imaging With Linear Sparse Periodic Array

Molaei

Skouroliakou

et al. 2022

IEEE Sensors J.

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The benefits of terahertz (THz) radiation have increased its use, especially in imaging systems. Recently, the use of a linear sparse periodic array (SPA) has been proposed as an effective solution for two-dimensional (2D) scanning in THz imaging systems. However, the special multistatic structure of the SPA is such that it is not possible to apply fast Fourier transform-based techniques directly in the near-field (NF). Therefore, in this paper, a fast processing approach based on two Fourier techniques compatible with linear SPA is presented for NF THz imaging. In this approach, we first employ a multistatic-to-monostatic conversion to reduce phase errors due to NF multistatic imaging. Then, to improve the quality of the results, we mathematically derive an interpolation formula to counteract the non-uniform spacing of the virtual array. The modified data is then processed by three rapid techniques (fast Fourier transform (FFT)-inverse fast Fourier transform, matched filtering and a novel 1D FFT-based technique with low computational complexity) to obtain reconstructed images of the scene. Numerical and experimental results confirm the satisfactory performance of the proposed approach in terms of both computational time and the quality of the reconstructed image.

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Two Dimensional Array Imaging With Beam Steered Data

Cited by 24 publications

References 14 publications

Fourier Compatible Near-Field Multiple-Input Multiple-Output Terahertz Imaging With Sparse Non-Uniform Apertures

Fourier Compatible Near-Field Multiple-Input Multiple-Output Terahertz Imaging With Sparse Non-Uniform Apertures

Lightweight FISTA-Inspired Sparse Reconstruction Network for mmW 3-D Holography

Fast Processing Approach for Near-Field Terahertz Imaging With Linear Sparse Periodic Array

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