scholarly journals Near-Field Three-Dimensional Planar Millimeter-Wave Holographic Imaging by Using Frequency Scaling Algorithm

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2438 ◽  
Author(s):  
Ye Zhang ◽  
Bin Deng ◽  
Qi Yang ◽  
Jingkun Gao ◽  
Yuliang Qin ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Near Field ◽  
Three Dimensional ◽  
Frequency Scaling ◽  
Holographic Imaging

Near-Field Three-Dimensional Planar Millimeter-Wave Holographic Imaging by Using Frequency Scaling Algorithm

Sensors ◽  
2017 ◽  
Vol 17 (11) ◽  
pp. 2438
Author(s):  
Ye Zhang ◽  
Bin Deng ◽  
Qi Yang ◽  
Jingkun Gao ◽  
Yuliang Qin ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Near Field ◽  
Three Dimensional ◽  
Frequency Scaling ◽  
Holographic Imaging

Exact Reconstruction for Near-Field Three-Dimensional Planar Millimeter-Wave Holographic Imaging

2015 ◽  
Vol 36 (12) ◽  
pp. 1221-1236 ◽  
Author(s):  
Lingbo Qiao ◽  
Yingxin Wang ◽  
Ziran Zhao ◽  
Zhiqiang Chen
Keyword(s):  
Millimeter Wave ◽  
Near Field ◽  
Three Dimensional ◽  
Holographic Imaging ◽  
Exact Reconstruction

Range Resolution Enhancement for Three-Dimensional Millimeter-Wave Holographic Imaging

2016 ◽  
Vol 15 ◽  
pp. 1422-1425 ◽  
Author(s):  
Lingbo Qiao ◽  
Yingxin Wang ◽  
Ziran Zhao ◽  
Zhiqiang Chen
Keyword(s):  
Millimeter Wave ◽  
Resolution Enhancement ◽  
Three Dimensional ◽  
Range Resolution ◽  
Holographic Imaging

Low-cost three-dimensional millimeter-wave holographic imaging system based on a frequency-scanning antenna

2017 ◽  
Vol 57 (1) ◽  
pp. A65
Author(s):  
Vahid Amin Nili ◽  
Ehsan Mansouri ◽  
Zahra Kavehvash ◽  
Mohammad Fakharzadeh ◽  
Mahdi Shabany ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Imaging System ◽  
Low Cost ◽  
Three Dimensional ◽  
Holographic Imaging ◽  
Frequency Scanning ◽  
Scanning Antenna

scholarly journals Enhanced Millimeter-Wave 3-D Imaging via Complex-Valued Fully Convolutional Neural Network

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 147
Author(s):  
Handan Jing ◽  
Shiyong Li ◽  
Ke Miao ◽  
Shuoguang Wang ◽  
Xiaoxi Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Millimeter Wave ◽  
Near Field ◽  
Back Propagation ◽  
Three Dimensional ◽  
Activation Function ◽  
Back Propagation Algorithm ◽  
Parametric Rectified Linear Unit ◽  
Complex Valued

To solve the problems of high computational complexity and unstable image quality inherent in the compressive sensing (CS) method, we propose a complex-valued fully convolutional neural network (CVFCNN)-based method for near-field enhanced millimeter-wave (MMW) three-dimensional (3-D) imaging. A generalized form of the complex parametric rectified linear unit (CPReLU) activation function with independent and learnable parameters is presented to improve the performance of CVFCNN. The CVFCNN structure is designed, and the formulas of the complex-valued back-propagation algorithm are derived in detail, in response to the lack of a machine learning library for a complex-valued neural network (CVNN). Compared with a real-valued fully convolutional neural network (RVFCNN), the proposed CVFCNN offers better performance while needing fewer parameters. In addition, it outperforms the CVFCNN that was used in radar imaging with different activation functions. Numerical simulations and experiments are provided to verify the efficacy of the proposed network, in comparison with state-of-the-art networks and the CS method for enhanced MMW imaging.

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