An improved IDW method for linear array 3D imaging sensor

2017 ◽  
Author(s):  
Mei Zhou ◽  
Hongcan Guan ◽  
Chuanrong Li ◽  
Geer Teng ◽  
Lian Ma
Keyword(s):  
3D Imaging ◽  
Linear Array ◽  
Imaging Sensor

scholarly journals A universal 3D imaging sensor on a silicon photonics platform

Nature ◽  
2021 ◽  
Vol 590 (7845) ◽  
pp. 256-261
Author(s):  
Christopher Rogers ◽  
Alexander Y. Piggott ◽  
David J. Thomson ◽  
Robert F. Wiser ◽  
Ion E. Opris ◽  
...  
Keyword(s):  
Silicon Photonics ◽  
3D Imaging ◽  
Imaging Sensor

scholarly journals Three-Dimensional Imaging of Terahertz Circular SAR with Sparse Linear Array

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2477 ◽  
Author(s):  
Jubo Hao ◽  
Jin Li ◽  
Yiming Pi
Keyword(s):  
3D Imaging ◽  
Linear Array ◽  
Ground Plane ◽  
Three Dimensional ◽  
Terahertz Waves ◽  
Recovery Method ◽  
Imaging Algorithm ◽  
Imaging Geometry ◽  
Imaging Results ◽  
Sparse Linear Array

Due to the non-contact detection ability of radar and the harmlessness of terahertz waves to the human body, three-dimensional (3D) imaging using terahertz synthetic aperture radar (SAR) is an efficient method of security detection in public areas. To achieve high-resolution and all aspect imaging, circular trajectory movement of radar and linear sensor array along the height direction were used in this study. However, the short wavelength of terahertz waves makes it practically impossible for the hardware to satisfy the half-wavelength spacing condition to avoid grating lobes. To solve this problem, a sparse linear array model based on the equivalent phase center principle was established. With the designed imaging geometry and corresponding echo signal model, a 3D imaging algorithm was derived. Firstly, the phase-preserving algorithm was adopted to obtain the 2D image of the ground plane for each sensor. Secondly, the sparse recovery method was applied to accomplish the scattering coefficient reconstruction along the height direction. After reconstruction of all the range-azimuth cells was accomplished, the final 3D image was obtained. Numerical simulations and experiments using terahertz radar were performed. The imaging results verify the effectiveness of the 3D imaging algorithm for the proposed model and validate the feasibility of terahertz radar applied in security detection.

scholarly journals 3D Imaging Sensor.

1992 ◽  
Vol 10 (2) ◽  
pp. 171-176 ◽  
Author(s):  
Kosuke SATO ◽  
Seiji INOKUCHI
Keyword(s):  
3D Imaging ◽  
Imaging Sensor

scholarly journals Downward-Looking Linear Array 3D SAR Imaging Based on Multiple Measurement Vectors Model and Continuous Compressive Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Qi-yong Liu ◽  
Qun Zhang ◽  
Fu-fei Gu ◽  
Yi-chang Chen ◽  
Le Kang ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
3D Imaging ◽  
Linear Array ◽  
Multiple Measurement ◽  
Memory Space ◽  
Multiple Measurement Vectors ◽  
Huge Data ◽  
Imaging Result ◽  
Sar Imaging ◽  
Cross Track

This paper concerns the problems of huge data and off-grid effect of cross-track direction in downward-looking linear array (DLLA) 3D SAR imaging. Since the 3D imaging needs a great deal of memory space, we consider the methods of downsampling to reduce the data quantity. In the azimuth direction, we proposed a method based on the multiple measurement vectors (MMV) model, which can enhance computational efficiency and elevate the performance of antinoise, to recover the signal. Further, in cross-track direction, since the resolution is restricted by the length of array, as well as platform size, the influence of off-grid effect is more serious than azimuth direction. Continuous compressive sensing (CCS), which can solve the off-grid effect of the classical compressive sensing (CS), is presented to obtain the precise imaging result under the noise scenarios. Finally, we validate our method by extension numerical experiments.

Simulation of a new 3D imaging sensor for identifying difficult military targets

2008 ◽  
Author(s):  
Christophe Harvey ◽  
Jonathan Wood ◽  
Peter Randall ◽  
Graham Watson ◽  
Gordon Smith
Keyword(s):  
3D Imaging ◽  
Imaging Sensor

New 3D imaging sensor: gaining range and texture

2002 ◽  
Author(s):  
Zonghua Zhang ◽  
David Zhang ◽  
Xiang Peng ◽  
Xiaotang Hu
Keyword(s):  
3D Imaging ◽  
Imaging Sensor
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