Abstract-Phaseless Terahertz Coded-Aperture Imaging Based on Incoherent Detection

Long Peng,  Chenggao Luo,  Bin Deng, Hongqiang Wang, Yuliang Qin,  Shuo Chen

https://www.mdpi.com/1424-8220/19/2/226

In this paper, we propose a phaseless terahertz coded-aperture imaging (PTCAI) method by using a single incoherent detector or an incoherent detection array. We at first analyze and model the system architecture, derive the matrix imaging equation, and then study the phase retrieval techniques to reconstruct the original target with high resolution. Numerical experiments are performed and the results show that the proposed method can significantly reduce the system complexity in the receiving process while maintaining high resolution imaging capability. Furthermore, the approach of using incoherent detection array instead of single detector is capable of decreasing the encoding and sampling times, and therefore helps to improve the imaging frame rate. In our future research, the method proposed in this paper will be experimentally tested and validated, and high-speed PTCAI at nearly real-time frame rates will be the main work.

Abstract-Three-Dimensional Terahertz Coded-Aperture Imaging in Space Domain

Bin Deng, Cheng-Gao Luo, Hong-Qiana Wang, Long Peng, Shuo Chen, Zhao-Wen Zhaung,

https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8386755

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. However, traditional three-dimensional (3D) TCAI working in time domain, has two problems, tkhat is, heavy computational burden caused by large-scale reference-signal matrix, and poor resolving ability under low signal-to-noise ratio (SNR). Frequency-domain (FD)-TCAI (FD-TCAI) is able to reduce the two problems only in a limited degree. Therefore, this paper first proposes a spacedomain (SD)-TCAI (SD-TCAI) method, which achieves more effective and efficient 3D imaging than FDTCAI under low SNR. With correlation operation, SD-TCAI transforms the echo signals from frequency domain to space domain, where spike pulses appear in the target positions. According to different spike pulses, the 3D imaging areas can be divided and reconstructed in parallel, and finally synthesized together to obtain the whole 3D target. In this way, the computational burden is further reduced, leading to an improved SNR. In this paper, we build and present the imaging models and procedures of TD-TCAI, FDTCAI, and SD-TCAI, respectively. The experimental results demonstrate that SD-TCAI has overwhelming superiority over TD-TCAI and FD-TCAI. Therefore, SD-TCAI has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc. 

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

Shuo Chen, Xiaoqiang Hua, Hongqiang Wang , Chenggao Luo,  Yongqiang Cheng and Bin Deng

www.mdpi.com/1424-8220/18/5/1582/pdf


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.

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

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

http://www.mdpi.com/1424-8220/18/5/1342

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. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc

Abstract-Three-Dimensional Terahertz Coded-Aperture Imaging Based on Single Input Multiple Output Technology

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

http://www.mdpi.com/1424-8220/18/1/303

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.