A Self-Calibration Methodology for Phased Array Antenna

This article proposes a self-calibration methodology for phased array antenna, which constructs a calibration base group of three units: a reference unit, an auxiliary unit, and a unit to be calibrated. Then, this work uses the practical electromagnetic parameters, introduces the concept of state, and utilizes reverse phase detection to enable faster calibration speeds and higher calibration accuracy. Furthermore, in the calibration process, this work carries out the cascade path optimization, which establishes the adjacency matrix between the antenna units and uses Dijkstra’s algorithm to compute the shortest cascade path between the reference antenna unit and the antenna unit to be calibrated so that greatly reduces the cascade error and improves the calibration accuracy. Using this methodology, there is a standard deviation error of about $1.4^{\circ}-1.8^{\circ}$ in the absence of Gaussian noise interference, and there is a standard deviation error of about $2.5^{\circ}-4.5^{\circ}$ in the averaging of multiple calculations in the presence of Gaussian noise interference.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1109/apcap62011.2024.10882043
Primary Topic
Antenna Design and Optimization
Type
article
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article

A Self-Calibration Methodology for Phased Array Antenna

Fei Xue, Yuxin Fan, Jianyi Zhou, Longhao Zheng
Antenna Design and Optimization
article

A Self-Calibration Methodology for Phased Array Antenna

Fei Xue, Yuxin Fan, Jianyi Zhou, Longhao Zheng
article en

Abstract

This article proposes a self-calibration methodology for phased array antenna, which constructs a calibration base group of three units: a reference unit, an auxiliary unit, and a unit to be calibrated. Then, this work uses the practical electromagnetic parameters, introduces the concept of state, and utilizes reverse phase detection to enable faster calibration speeds and higher calibration accuracy. Furthermore, in the calibration process, this work carries out the cascade path optimization, which establishes the adjacency matrix between the antenna units and uses Dijkstra’s algorithm to compute the shortest cascade path between the reference antenna unit and the antenna unit to be calibrated so that greatly reduces the cascade error and improves the calibration accuracy. Using this methodology, there is a standard deviation error of about $1.4^{\circ}-1.8^{\circ}$ in the absence of Gaussian noise interference, and there is a standard deviation error of about $2.5^{\circ}-4.5^{\circ}$ in the averaging of multiple calculations in the presence of Gaussian noise interference.

State Key Laboratory of Millimeter Waves, Southeast University (CN)
Affordable and clean energy
Openalex Percentile: Top 54%
Antenna Design and Optimization
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