scholarly journals Calibration to Mitigate Near-Field Antennas Effects for a MIMO Radar Imaging System

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 514
Author(s):  
Ha Hoang ◽  
Matthias John ◽  
Patrick McEvoy ◽  
Max J. Ammann
Keyword(s):  
High Resolution ◽  
Test Pattern ◽  
Imaging System ◽  
Near Field ◽  
Calibration Method ◽  
Radar Imaging ◽  
Mimo Radar ◽  
Calibration Process ◽  
Position Shift

A calibration method for a high-resolution hybrid MIMO turntable radar imaging system is presented. A line of small metal spheres is employed as a test pattern in the calibration process to measure the position shift caused by undesired antenna effects. The unwanted effects in the antenna near-field responses are analysed, modelled and significantly mitigated based on the symmetry and differences in the responses of the MIMO configuration.

scholarly journals 3DRIED: A High-Resolution 3-D Millimeter-Wave Radar Dataset Dedicated to Imaging and Evaluation

2021 ◽  
Vol 13 (17) ◽  
pp. 3366
Author(s):  
Shunjun Wei ◽  
Zichen Zhou ◽  
Mou Wang ◽  
Jinshan Wei ◽  
Shan Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Millimeter Wave ◽  
Imaging System ◽  
Near Field ◽  
Center Frequency ◽  
W Band ◽  
Wave Radar ◽  
Imaging Results ◽  
Different Types ◽  
Imaging Algorithms

Millimeter-wave (MMW) 3-D imaging technology is becoming a research hotspot in the field of safety inspection, intelligent driving, etc., due to its all-day, all-weather, high-resolution and non-destruction feature. Unfortunately, due to the lack of a complete 3-D MMW radar dataset, many urgent theories and algorithms (e.g., imaging, detection, classification, clustering, filtering, and others) cannot be fully verified. To solve this problem, this paper develops an MMW 3-D imaging system and releases a high-resolution 3-D MMW radar dataset for imaging and evaluation, named as 3DRIED. The dataset contains two different types of data patterns, which are the raw echo data and the imaging results, respectively, wherein 81 high-quality raw echo data are presented mainly for near-field safety inspection. These targets cover dangerous metal objects such as knives and guns. Free environments and concealed environments are considered in experiments. Visualization results are presented with corresponding 2-D and 3-D images; the pixels of the 3-D images are 512×512×6. In particular, the presented 3DRIED is generated by the W-band MMW radar with a center frequency of 79GHz, and the theoretical 3-D resolution reaches 2.8 mm × 2.8 mm × 3.75 cm. Notably, 3DRIED has 5 advantages: (1) 3-D raw data and imaging results; (2) high-resolution; (3) different targets; (4) applicability for evaluation and analysis of different post processing. Moreover, the numerical evaluation of high-resolution images with different types of 3-D imaging algorithms, such as range migration algorithm (RMA), compressed sensing algorithm (CSA) and deep neural networks, can be used as baselines. Experimental results reveal that the dataset can be utilized to verify and evaluate the aforementioned algorithms, demonstrating the benefits of the proposed dataset.

scholarly journals An Imaging Plane Calibration Method for MIMO Radar Imaging

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5261
Author(s):  
Yuanyue Guo ◽  
Bo Yuan ◽  
Zhaohui Wang ◽  
Rui Xia
Keyword(s):  
Multiple Input Multiple Output ◽  
Calibration Method ◽  
Radar Imaging ◽  
Spatial Spectrum ◽  
Mimo Radar ◽  
Cooperative Movement ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Calibration Algorithm ◽  
Imaging Plane

In two dimensional cross-range multiple-input multiple-output radar imaging for aerial targets, due to the non-cooperative movement of the targets, the estimated imaging plane parameters, namely the center and the posture angles of the imaging plane, may have deviations from true values, which defocus the final image. This problem is called imaging plane mismatch in this paper. Focusing on this problem, firstly the deviations of spatial spectrum fulfilling region caused by imaging plane mismatch is analyzed, as well as the errors of the corresponding spatial spectral values. Thereupon, the calibration operation is deduced when the imaging plane parameters are accurately obtained. Afterwards, an imaging plane calibration algorithm is proposed to utilize particle swarm optimization to search out the imaging plane parameters. Finally, it is demonstrated through simulations that the proposed algorithm can accurately estimate the imaging plane parameters and achieve good image focusing performance.

scholarly journals Phase-Controlled Planar Metalenses for High-Resolution Terahertz Focusing

Photonics ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 143
Author(s):  
Xin Yu ◽  
Yun Shen ◽  
Guohong Dai ◽  
Liner Zou ◽  
Tailin Zhang ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Imaging System ◽  
Incident Light ◽  
Near Field ◽  
Numerical Aperture ◽  
Full Width ◽  
Half Maximum ◽  
Near Field Imaging ◽  
Field Imaging

We experimentally demonstrate that high-resolution terahertz focusing can be realized in planar metalenses, which consist of arrays of different V-shaped antenna units on a silicon substrate. Numerical results show that a larger numerical aperture of metalenses can provide smaller full width at half maximum of field distribution, leading to higher spatial resolution. The measurement of fabricated metalenses samples was performed by a terahertz near-field imaging system, and experimental results agree well with the numerical prediction. Especially for 1.1 THz incident light, when the numerical aperture increases from 0.79 to 0.95, the full width at half maximum correspondingly decreases from 343 μm to 206 μm, offering an improvement of spatial resolution.

scholarly journals Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar

2021 ◽  
Vol 11 (19) ◽  
pp. 8926
Author(s):  
Jie Liu ◽  
Kai Zhang ◽  
Zhenlin Sun ◽  
Qiang Wu ◽  
Wei He ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Imaging System ◽  
Learning Algorithm ◽  
Layer Structure ◽  
Near Field ◽  
Radar Imaging ◽  
Image Resolution ◽  
Sar Images ◽  
Millimeter Wave Radar ◽  
Wave Radar

At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and synthetic aperture radar techniques are used to obtain the raw data. The analytical Fourier transform algorithm is used for image reconstruction. When imaging a target at 90 mm from radar, which belongs to the near field imaging scene, the image resolution can reach 1.90 mm in X-direction and 1.73 mm in Y-direction. Since the error caused by the distance between radar and target will lead to noise, the original reconstruction image is processed by gamma transform, which eliminates image noise, then the image is enhanced by linearly stretched transform to improve visual recognition, which lays a good foundation for supervised learning. In order to flexibly deploy the machine learning algorithm in various application scenarios, ShuffleNetV2, MobileNetV3 and GhostNet representative of lightweight convolutional neural networks with redefined convolution, branch structure and optimized network layer structure are used to distinguish multi-category SAR images. Through the fusion of squeeze-and-excitation and the selective kernel attention mechanism, more precise features are extracted for classification, the proposed GhostNet_SEResNet56 can realize the best classification accuracy of SAR images within limited resources, which prediction accuracy is 98.18% and the number of parameters is 0.45 M.

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