ArticleSensors (Basel, Switzerland)2018
Three-Dimensional Terahertz Coded-Aperture Imaging Based on Single Input Multiple Output Technology.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Effect of Imaging Range on Performance of Terahertz Coded-Aperture Imaging.Sensors (Basel, Switzerland) · 2025Article
- Phaseless Terahertz Coded-Aperture Imaging for Sparse Target Based on Phase Retrieval Algorithm.Sensors (Basel, Switzerland) · 2019Article
- Phaseless Terahertz Coded-Aperture Imaging Based on Incoherent Detection.Sensors (Basel, Switzerland) · 2019Article
- Three-Dimensional Terahertz Coded-Aperture Imaging Based on Back Projection.Sensors (Basel, Switzerland) · 2018Article
- Three-Dimensional Terahertz Coded-Aperture Imaging Based on Geometric Measures.Sensors (Basel, Switzerland) · 2018Article
- Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. In this paper, we propose a three-dimensional (3D) TCAI architecture based on single input multiple output (SIMO) technology, which can reduce the coding and sampling times sharply. The coded aperture applied in the proposed TCAI architecture loads either purposive or random phase modulation factor. In the transmitting process, the purposive phase modulation factor drives the terahertz beam to scan the divided 3D imaging cells. In the receiving process, the random phase modulation factor is adopted to modulate the terahertz wave to be spatiotemporally independent for high resolution. Considering human-scale targets, images of each 3D imaging cell are reconstructed one by one to decompose the global computational complexity, and then are synthesized together to obtain the complete high-resolution image. As for each imaging cell, the multi-resolution imaging method helps to reduce the computational burden on a large-scale reference-signal matrix. The experimental results demonstrate that the proposed architecture can achieve high-resolution imaging with much less time for 3D targets and has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.
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Registered trials
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