Microwave Imaging in Security — Two Decades of Innovation

Ahmed, Sherif Sayed

doi:10.1109/jmw.2020.3035790

Cited by 69 publications

(27 citation statements)

References 38 publications

(27 reference statements)

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“…In addition to the numerical testing to characterize the classification accuracy, in order to demonstrate that the trained classifier model performs accurately with experimental mmW data, mmW images of real threat data are also considered and used as test samples. For this experimental demonstration, a millimetre-wave handheld imager [73] based on a multistatic sparse array [74], [75] is used to image a scissor and a knife, the setup of which is shown in Fig. 8(a).…”

Section: A Classification Resultsmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

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A millimeter-wave (mmW) classifier system applied to images synthesized from a codedaperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.INDEX TERMS Millimetre-wave, imaging radars, computational imaging, neural networks, image classification, coded-aperture.

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Section: A Classification Resultsmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

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“…For example, Fig. 3 shows the perspective view and top view of a PSF derived from (1). The first null implies the cross range spatial resolution while the side lobe level predicts how likely the artifacts arise.…”

Section: Sequencementioning

confidence: 99%

“…M ICROWAVE and sub-millimeter wave imaging for target detection has been developed in last two decades, it faces low spatial resolution due to low operation frequency [1]- [6]. THz wave, referring to the frequency band between 100 GHz and 10 THz, has a short wavelength, non-ionizing characteristic and strong penetrating ability which is promising to applications for target detection or personnel screening [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Advanced THz MIMO Sparse Imaging Scheme Using Multipass Synthetic Aperture Focusing and Low-Rank Matrix Completion Techniques

Shu

Alfadhl

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This paper proposes an advanced THz multipleinput-multiple-output (MIMO) near-field sparse imaging scheme for target detection that uses single-pass synthetic aperture focusing and multi-pass interferometric synthetic aperture focusing techniques to improve imaging effectiveness and efficiency. It benefits from the MIMO of linear sparse periodic array (SPA) and random sparse imaging to reduce sampling data as well as system cost. Both simulated and proof-of-concept experimental results have verified the scheme, revealing that the single-pass synthetic aperture imaging approach is sufficient to identify pure metallic targets with 3.87% of λ/2 sampling data. And multi-pass interferometric synthetic aperture imaging approach is capable of improving image quality on image signal to noise ratio and contrast, which is ideal for detecting more challenging targets. The random sparse imaging with help of low rank matrix completion (LRMC) technique has shown promising potential of achieving equivalent image quality when further reducing 43.75% data in the experiment of 5-pass imaging on a complex target, this corresponds to 82.51% data reduction compared to λ/2 sampling.

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“…M ILLIMETER-and submillimeter-waves feature unique properties in the electromagnetic spectrum, which can be used to address measurement demands for nondestructive testing and remote sensing, for example, in production processes [1], or for security applications, for example, in the detection of optically hidden objects [2], [3]. The wavelength in the submillimeter-wave domain is long enough to penetrate optically opaque obstructions, while simultaneously being small enough to enable high-resolution imaging.…”

Section: Introductionmentioning

confidence: 99%

“…In order to reduce measurement time, electronic-or frequency-scanning approaches may be utilized. Although eliminating the need for slow mechanically moving parts partially or completely, they feature complex system designs, for example, in the needed amount of hardware channels to enable high-resolution imaging [2]. This letter reports on a submillimeter-wave RAR imaging system, featuring FMCW radar measurements with wideband operation in the frequency range of 343 GHz to 423 GHz, resulting in a total bandwidth of 80 GHz.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution 400 GHz Submillimeter-Wave Quasi-Optical Radar Imaging System

Baumann

Gashi

Meier

et al. 2022

IEEE Microw. Wireless Compon. Lett.

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A submillimeter-wave quasi-optical high-resolution imaging system, based on a frequency-modulated continuous wave radar sensor operating between 343 GHz and 423 GHz utilizing a bandwidth of 80 GHz, is presented. The system setup in-tandem with the essential building blocks is described and the generation of high-resolution power-and elevation-profile images of various samples under test is presented. Experimental results show a lateral resolution limit in the order of wavelength.

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Microwave Imaging in Security — Two Decades of Innovation

Cited by 69 publications

References 38 publications

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

Advanced THz MIMO Sparse Imaging Scheme Using Multipass Synthetic Aperture Focusing and Low-Rank Matrix Completion Techniques

High-Resolution 400 GHz Submillimeter-Wave Quasi-Optical Radar Imaging System

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