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Phaseless Spherical Near-Field to Far-Field Transformation Algorithm via Sparsity of Spherical Mode Coefficients
Author(s) -
Jiaqi Wang,
Yinghong Wen,
Dan Zhang,
Jinbao Zhang
Publication year - 2022
Publication title -
international journal of antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.282
H-Index - 37
eISSN - 1687-5877
pISSN - 1687-5869
DOI - 10.1155/2022/2428939
Subject(s) - oversampling , algorithm , transformation (genetics) , near and far field , sampling (signal processing) , transformation matrix , radiation pattern , antenna (radio) , computer science , phase (matter) , mode (computer interface) , mathematics , optics , physics , bandwidth (computing) , filter (signal processing) , telecommunications , computer vision , kinematics , operating system , biochemistry , chemistry , gene , quantum mechanics , classical mechanics
In order to overcome the difficulty in obtaining accurate phase information in antenna near-field measurement, this study proposes a phase recovery algorithm that can be applied to phaseless spherical near-field sampling data. Based on the sparsity of spherical mode coefficients (SMCs), under the condition of unknown sparsity level and low oversampling rate, the proposed algorithm can accurately recover SMC to obtain high-precision antenna radiation characteristics. To improve the recovery performance of the algorithm, a spherical sampling strategy matching with the algorithm is also presented. By calculating the correlation of the measurement matrix and comparing the accuracy of the reconstructed far field, a set of optimal parameter settings including the number of spheres, the distribution of the measurement points, radius difference, and polarization mode is determined. On the basis of the obtained measurement data, the performance advantage of the presented algorithm in recovering SMC is demonstrated.

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