Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network

Chen, Shuo; Luo, Chenggao; Wang, Hongqiang; Deng, Bin; Cheng, Yongqiang; Zhuang, Zhihao

doi:10.3390/s18051342

Cited by 11 publications

(11 citation statements)

References 26 publications

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“…[ 19 , 20 ] transforms the EV and RSM into range domain. Essentially, the transformation is the process of pulse compression, which can improve the SNR in a certain degree and distinguish the target information in different ranges [ 17 ]. However, the range information is difficult to be detected when the SNR is under −10 dB.…”

Section: Imaging Methodsmentioning

confidence: 99%

“…To solve the problems, Ref. [ 17 ] proposes an imaging method to obtain the range profile with matched filtering, which belongs to pulse compression. However, when the SNR is under -10dB, pulse compression has new problem of extracting the true target positions form the range profile and thus results in the imaging failure.…”

Section: Introductionmentioning

confidence: 99%

“…[ 16 ] and traditional TCAIs are time-domain TCAIs while Refs. [ 17 , 18 ] belong to range-domain TCAI.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Terahertz Coded-Aperture Imaging Based on Back Projection

Chen

Luo

Wang

et al. 2018

Sensors

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Terahertz coded-aperture imaging (TCAI) can overcome the difficulties of traditional radar in forward-looking and high-resolution imaging. Three-dimensional (3D) TCAI relies mainly on the reference-signal matrix (RSM), the large size and poor accuracy of which reduce the computational efficiency and imaging ability, respectively. According to the previous research on TCAI, traditional TCAI cannot reduce the heavy computational burden while the improved TCAI achieve reconstructing the target parts of different ranges in parallel. However, large-sized RSM still accounts for the computational complexity of traditional TCAI and the improved TCAI. Therefore, this paper proposes a more efficient imaging method named back projection (BP)-TCAI (BP-TCAI). Referring to the basic principle of BP, BP-TCAI can not only divide the scattering information in different ranges but also project the range profiles into different imaging subareas. In this way, the target parts in different subareas can be reconstructed simultaneously to synthesize the whole 3D target and thus decomposes the computational complexity thoroughly. During the pulse compression and projection processes, the signal-to-noise ratio (SNR) of BP-TCAI is also improved. This present the imaging method, model and procedures of traditional TCAI, the improved TCAI and the proposed BP-TCAI. Numerical experimental results prove BP-TCAI to be more effective and efficient than previous imaging methods of TCAI. Besides, BP-TCAI can also be seen as synthetic aperture radar (SAR) imaging with coding technology. Therefore, BP-TCAI opens a future gate combining traditional SAR and coded-aperture imaging.

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Section: Imaging Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Terahertz Coded-Aperture Imaging Based on Back Projection

Chen

Luo

Wang

et al. 2018

Sensors

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“…Active terahertz video surveillance is historically associated with such areas as radar and holography [39,41]. At present, in the field of active terahertz observation, it has been proposed to use the mechanical and digital beam formation [20,42,43], terahertz holography [44,45], time-of-flight terahertz tomography [46], synthesized apertures [47] to obtain information on the threedimensional surface of the object under study -49], coding apertures [50][51][52][53][54], frequency modulation of the signal [55], antennas from metamaterials [56], etc.…”

Section: Combining the Capabilities Of Thz And 3d Video: The History mentioning

confidence: 99%

Semantic Fusion and Joint Analysis of Terahertz and 3d Video Images by the Means of Object-Oriented Logic Programming

Morozov¹,

Sushkova²,

Polupanov³

et al. 2018

RENSIT

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The aim of the work is to create methods and software tools for the terahertz range intelligent video surveillance, that is, for automatic analysis of video images in the terahertz frequency range. Terahertz video surveillance provides unique prospects in the area of public safety; in particular, it allows you to remotely identify weapons and dangerous items hidden under clothing on the human body. However, such characteristics of terahertz video, as low resolution, low contrast, and low signal-to-noise ratio, lead to the need to develop new methods and approaches to automatic video analysis. To understand the terahertz video image, the operator of the industrial video surveillance system usually compares it with images in other frequency ranges (visible or infrared). The comparison of these images of different types helps the operator to interpret the colored and/ or one-color spots in the terahertz image in a proper way. In terms of automatic video analysis, this means that the context of the observed events and objects is taken into account, or in other words, a semantic fusion is implemented of the terahertz range video image with video images of other frequency ranges, e.g., near-infrared, visible, etc. The authors consider the semantic fusion of the video images as a critical component of the prospective terahertz intelligent video surveillance technology. A new method of the terahertz video surveillance based on the fusion of the terahertz video with 3D video is proposed. The means of the object-oriented logic programming developed for the semantic fusion of the terahertz and 3D video images are described. The developed method provides a real-time fusion of the terahertz video acquired using the THERZ-7A (Astrohn Technology Ltd) subterahertz scanning device (0.23-0.27 THz) and 3D video data acquired using the Kinect 2 (Microsoft Inc) time-of-flight sensor. The method and software tools for the semantic fusion of the terahertz and 3D video images are developed.

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“…At extremely low SNRs, the useful signal is drowned in noise, thus leading to a mismatch between the back signal and reference signal matrices. Recently, an imaging method was proposed to obtain the range profile with matched filtering [ 18 ], which is a step in pulse compression. However, when the SNR is under −10 dB, pulse compression has the additional problem of having to extract true target positions from the range profile, thus resulting in imaging failure.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Geometric Measures

Chen

Hua

Wang

et al. 2018

Sensors

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For synthetic aperture radars, it is difficult to achieve forward-looking and staring imaging with high resolution. Fortunately, terahertz coded-aperture imaging (TCAI), an advanced radar imaging technology, can solve this problem by producing various irradiation patterns with coded apertures. However, three-dimensional (3D) TCAI has two problems, including a heavy computational burden caused by a large-scale reference signal matrix, and poor resolving ability at low signal-to-noise ratios (SNRs). This paper proposes a 3D imaging method based on geometric measures (GMs), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. At extremely low SNRs, it is difficult to detect the range cells containing scattering information with an ordinary range profile. However, this difficulty can be overcome through GMs, which can enhance the useful signal and restrain the noise. By extracting useful data from the range profile, target information in different imaging cells can be simultaneously reconstructed. Thus, the computational complexity is distinctly reduced when the 3D image is obtained by combining reconstructed 2D imaging results. Based on the conventional TCAI (C-TCAI) model, we deduce and build a GM-based TCAI (GM-TCAI) model. Compared with C-TCAI, the experimental results demonstrate that GM-TCAI achieves a more impressive performance with regards to imaging ability and efficiency. Furthermore, GM-TCAI can be widely applied in close-range imaging fields, for instance, medical diagnosis, nondestructive detection, security screening, etc.

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Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network

Cited by 11 publications

References 26 publications

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Back Projection

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Back Projection

Semantic Fusion and Joint Analysis of Terahertz and 3d Video Images by the Means of Object-Oriented Logic Programming

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Geometric Measures

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