A Novel Two-Dimensional Sparse MIMO Array Topology for UWB Short-Range Imaging

Tan, Kui‐Thong; Wu, Shiyou; Wang, Youcheng; Ye, Shengbo; Chen, Jie; Fang, Guangyou

doi:10.1109/lawp.2015.2469759

Cited by 41 publications

(8 citation statements)

References 6 publications

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“…To compute this cost function, matrix Σ e is required to be updated at each iteration using the current observation matrix A Γ and Eqn. (9). In order to analyze the computational complexity of the CSBS algorithm, recall that Σ e and the summation in Eqn.…”

Section: Optimal Design Methodsmentioning

confidence: 99%

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Optimal design of sparse MIMO arrays for near-field ultrawideband imaging

Kocamis

Oktem

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Near-field ultrawideband imaging is a promising remote sensing technique in various applications such as airport security, surveillance, medical diagnosis, and through-wall imaging. Recently, there has been increasing interest in using sparse multiple-input-multiple-output (MIMO) arrays to reduce hardware complexity and cost. In this paper, based on a Bayesian estimation framework, an optimal design method is presented for two-dimensional MIMO arrays in ultrawideband imaging. The optimality criterion is defined based on the image reconstruction quality obtained with the design, and the optimization is performed over all possible locations of antenna elements using an algorithm called clustered sequential backward selection algorithm. The designs obtained with this approach are compared with that of some commonly used sparse array configurations in terms of image reconstruction quality for various noise levels.

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Section: Optimal Design Methodsmentioning

confidence: 99%

“…Computation of Σ e involves matrix multiplication and inversion operations in Eqn. (9). Assuming that the number of rows in the updated matrix A Γ is Q (Q ≤ M T F ), the computational complexity is O(Q 2 N + N 2 Q) for the matrix multiplications and O(N 3 ) for the matrix inversion.…”

Section: Optimal Design Methodsmentioning

confidence: 99%

Optimal design of sparse MIMO arrays for near-field ultrawideband imaging

Kocamis

Oktem

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…However, recently there has been growing interest in using sparse multiple-input multiple-output (MIMO) arrays with spatially distributed transmit and receive antennas (i.e. multistatic array) because these can offer high resolution with reduced hardware complexity, cost, and acquisition time [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Sparsity-based three-dimensional image reconstruction for near-field MIMO radarimaging

Oktem¹

2019

Turk J Elec Eng & Comp Sci

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Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, airport security, concealed weapon detection, and surveillance. The successful operation of these radar imaging systems highly depends on the quality of the images reconstructed from radar data.Since the underlying scenes can be typically represented sparsely in some transform domain, sparsity priors can effectively regularize the image formation problem and hence enable high-quality reconstructions. In this paper, we develop an efficient three-dimensional image reconstruction method that exploits sparsity in near-field MIMO radar imaging.Sparsity is enforced using total variation regularization, and the reflectivity distribution is reconstructed iteratively without requiring computation with huge matrices. The performance of the developed algorithm is illustrated through numerical simulations. The results demonstrate the effectiveness of the sparsity-based method compared to a classical image reconstruction method in terms of image quality.

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“…However, the budget and complexity of a large-sized system are invariably determining factors in some applications, e.g., in a human THz imager with a size of 1 m × 0.5 m, such a detector array might not be practical. Another mode pertaining to conventional optics and microwaves [ 22 ] is readily deployable without drastic modifications to realize image reconstruction: an antenna array could be utilized whose elements can be reduced substantially by the employment of the multi-input/multi-output (MIMO) technology [ 23 , 24 , 25 ], or the sparse array technology [ 24 , 25 , 26 , 27 ]. Moreover, as the frequency band we considered in the experiments is in the millimeter wave region (W-band, 75–110 GHz) of the electromagnetic spectrum, which is in the low THz region of frequency, the method of three-dimensional electromagnetic imaging is available as well.…”

Section: Introductionmentioning

confidence: 99%

A High Precision Terahertz Wave Image Reconstruction Algorithm

Guo

Chang

Geng

et al. 2016

Sensors

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With the development of terahertz (THz) technology, the applications of this spectrum have become increasingly wide-ranging, in areas such as non-destructive testing, security applications and medical scanning, in which one of the most important methods is imaging. Unlike remote sensing applications, THz imaging features sources of array elements that are almost always supposed to be spherical wave radiators, including single antennae. As such, well-developed methodologies such as Range-Doppler Algorithm (RDA) are not directly applicable in such near-range situations. The Back Projection Algorithm (BPA) can provide products of high precision at the the cost of a high computational burden, while the Range Migration Algorithm (RMA) sacrifices the quality of images for efficiency. The Phase-shift Migration Algorithm (PMA) is a good alternative, the features of which combine both of the classical algorithms mentioned above. In this research, it is used for mechanical scanning, and is extended to array imaging for the first time. In addition, the performances of PMA are studied in detail in contrast to BPA and RMA. It is demonstrated in our simulations and experiments described herein that the algorithm can reconstruct images with high precision.

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A Novel Two-Dimensional Sparse MIMO Array Topology for UWB Short-Range Imaging

Cited by 41 publications

References 6 publications

Optimal design of sparse MIMO arrays for near-field ultrawideband imaging

Optimal design of sparse MIMO arrays for near-field ultrawideband imaging

Sparsity-based three-dimensional image reconstruction for near-field MIMO radarimaging

A High Precision Terahertz Wave Image Reconstruction Algorithm

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