An Effective Antenna Pattern Reconstruction Method for Planar Near-Field Measurement System

Zheng, Junhao; Chen, Xiaoming; Huang, Yi

doi:10.1109/tim.2022.3193194

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…After obtaining the cluster classification results, the initial near-field sampling data is sorted according to the sample sampling density and the rate of variation of adjacent samples, and the top ranking represents the area that needs to be interpolated. In order to calculate the sampling density, the initial sampling points are represented by a Voronoi diagram according to the nearest neighbor principle [27]. Each initial sampling point corresponds to a cell, and the sampling density is determined by the area of each cell.…”

Section: Background Theorymentioning

confidence: 99%

Combined Application of Partition Clustering Classification and Gerchberg-Papoulis Optimization Algorithm for Spherical Near Field Antenna Measurements

Peng

Ren

et al. 2023

ACES Journal

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An adaptive sampling and optimized extrapolation scheme for spherical near-field antenna testing is proposed. The method relies on the partition clustering classification algorithm and Voronoi classification to divide a small amount of initial data into subclasses and cells. The sampling density and rates of variation between adjacent sampling points are used as an overall metric function to evaluate the sampling dynamics at each location. Appropriate interpolation is performed in the highly dynamic region to increase the effective data in the near-field samples. The Gerchberg-Papoulis algorithm extrapolates the unnecessary interpolation region to improve the near-field sampling accuracy. This method uses a small amount of initial near-field sampled data for near-far field conversion to achieve the same precision as uniform oversampling. The feasibility and stability of the algorithm are proved from the actual measurement results.

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Section: Background Theorymentioning

confidence: 99%

Combined Application of Partition Clustering Classification and Gerchberg-Papoulis Optimization Algorithm for Spherical Near Field Antenna Measurements

Peng

Ren

et al. 2023

ACES Journal

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“…Traditional near-field measurement techniques for antennas encompass plane scanning, cylindrical scanning, spherical scanning, and polar plane scanning [ 3 ]. These methods rely on mechanical displacement structures to measure the microwave near-fields between the probe and monopole antennas [ 4 ]. However, achieving more precise microwave near-field data necessitates increasing the sampling points during scanning, resulting in significantly longer measurement times [ 5 , 6 ] and poor response detection effects from the input antenna pulse signal [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Near-Field Microwave Imaging Method of Monopole Antennas Based on Nitrogen-Vacancy Centers in Diamond

Jia,

Qin,

Luo

et al. 2024

Micromachines

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Visualizing the near-field distribution of microwave field in a monopole antenna is very important for antenna design and manufacture. However, the traditional method of measuring antenna microwave near field distribution by mechanical scanning has some problems, such as long measurement time, low measurement accuracy and large system volume, which seriously limits the measurement effect of antenna microwave near field distribution. In this paper, a method of microwave near-field imaging of a monopole antenna using a nitrogen-vacancy center diamond is presented. We use the whole diamond as a probe and camera to achieve wide-field microwave imaging. Because there is no displacement structure in the system, the method has high time efficiency and good stability. Compared with the traditional measurement methods, the diamond probe has almost no effect on the measured microwave field, which realizes the accurate near-field imaging of the microwave field of the monopole antenna. This method achieves microwave near-field imaging of a monopole antenna with a diameter of 100 µm and a length of 15 mm at a field of view of 5 × 5 mm, with a spatial resolution of 3 µm and an imaging bandwidth of 2.7~3.2 GHz, and an optimal input microwave phase resolution of 0.52° at a microwave power of 0.8494 W. The results provide a new method for microwave near-field imaging and measurement of monopole antennas.

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“…This conventional approach relies on the mechanical rasterscan of a sampling probe to capture the AUT radiated fields, which can be time-consuming [8]. To address this challenge, several methods such as reduced sampling [9], [10], adaptive sampling [11], [12], clustering analysis [13], [14], [15], and machine learning [16] are proposed to balance the efficiency and accuracy of the NF measurement. However, these methods are still confined to probe-based raster-scanning approaches.…”

Section: Introductionmentioning

confidence: 99%

Microwave Computational Imaging-Based Near-Field Measurement Method

Zhao,

Zhang,

et al. 2024

Antennas Wirel. Propag. Lett.

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An Effective Antenna Pattern Reconstruction Method for Planar Near-Field Measurement System

Cited by 15 publications

References 25 publications

Combined Application of Partition Clustering Classification and Gerchberg-Papoulis Optimization Algorithm for Spherical Near Field Antenna Measurements

Combined Application of Partition Clustering Classification and Gerchberg-Papoulis Optimization Algorithm for Spherical Near Field Antenna Measurements

Near-Field Microwave Imaging Method of Monopole Antennas Based on Nitrogen-Vacancy Centers in Diamond

Microwave Computational Imaging-Based Near-Field Measurement Method

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