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Neural Network-Based Phase Estimation for Antenna Array Using Radiation Power Pattern
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DOI：10.1109/LAWP.2022.3167697.png)
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Abstract
En 中文
In this letter, a neural network-based interelement phase estimation method using radiation power pattern of the linear phased array is proposed. To validate the proposed method, a radiation pattern measured in an anechoic chamber is input to the neural network to estimate the initial phase errors, and to confirm practical estimation accuracy. The proposed method requires only single radiation pattern measurement and no additional measurements only for estimation. This indicates the proposed method is significantly more time-saving, compared to other conventional techniques. Furthermore, we propose a method to suppress the failure rate of estimation by recursively reinputting patterns into the neural network, and discuss its effectiveness. These results show that the proposed methods useful for phase estimation of the linear array in experiments.
Keywords:
Antenna radiation patterns
Artificial neural networks
Calibration
Antenna measurements
Phased arrays
Training
Power measurement
Antenna arrays
calibration
deep learning
neural network (NN)
radiation patterns
Journal
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4.8
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1.0W
Citations:
2.8W
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