A Deep Learning Based Antenna Array Calibration Method Using Radiation Power Pattern

This paper reports a calibration method for excitation parameters of antenna arrays based on deep learning of radiated power patterns. Our method using the trained neural network requires a single radiation pattern obtained via power-only measurement before calibration and no other additional measur...

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Bibliographic Details
Main Authors: Iye, Tetsuya, Susukida, Yuki, Takaya, Shohei, Sugiura, Tomoki, Fujii, Yoshimi
Format: Conference Proceeding
Language:English
Subjects:
antenna arrays
Antenna measurements
Artificial neural networks
Calibration
Deep learning
Mobile antennas
neural network
Phase estimation
Phased arrays
radiation pattern
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Summary:This paper reports a calibration method for excitation parameters of antenna arrays based on deep learning of radiated power patterns. Our method using the trained neural network requires a single radiation pattern obtained via power-only measurement before calibration and no other additional measurement. Nevertheless, the method yields an immediate result of estimated imbalances of excitation amplitude and phase values for antenna elements on the array, respectively, with higher accuracy than the conventional methods. The proposed method enables fast and accurate calibration of an antenna array even without a self-calibration circuit.
ISSN:2166-9589
DOI:10.1109/PIMRC54779.2022.9977899