Neural Network-Based Phase Estimation for Antenna Array Using Radiation Power Pattern
In this paper, a neural network-based inter-element 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,...
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| Veröffentlicht in: | IEEE antennas and wireless propagation letters 2022-07, Vol.21 (7), p.1-1 |
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| container_title | IEEE antennas and wireless propagation letters |
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| creator | Iye, Tetsuya Wyk, Pieter van Matsumoto, Takahiro Susukida, Yuki Takaya, Shohei Fujii, Yoshimi |
| description | In this paper, a neural network-based inter-element 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 before calibration and no additional measurements only for calibration. 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 re-inputting 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. |
| doi_str_mv | 10.1109/LAWP.2022.3167697 |
| format | Article |
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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 before calibration and no additional measurements only for calibration. 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 re-inputting patterns into the neural network, and discuss its effectiveness. 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These results show that the proposed methods useful for phase estimation of the linear array in experiments.</description><subject>Anechoic chambers</subject><subject>Antenna arrays</subject><subject>Antenna measurements</subject><subject>Antenna radiation patterns</subject><subject>Artificial neural networks</subject><subject>Calibration</subject><subject>deep learning</subject><subject>Failure rates</subject><subject>Linear arrays</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Phased arrays</subject><subject>Power measurement</subject><subject>radiation patterns</subject><subject>Training</subject><issn>1536-1225</issn><issn>1548-5757</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQhhdRsFZ_gHhZ8Jy6H9nZ5hiLX1BqEIvHZZtMNLVu6u6W0n9vQoqXeefwzAzzEHLN2YRzlt3N849iIpgQE8lBQ6ZPyIirdJoorfRp30tIuBDqnFyEsGaMa1ByRJYL3Hm7oQuM-9Z_J_c2YEWLry7oQ4jNj41N62jdepq7iM5ZmntvD3QZGvdJ32zVDETR7tHTwsaI3l2Ss9puAl4dc0yWjw_vs-dk_vr0MsvnSSkAYrKSCCWmgBValVqV1dKWUnRVcMFQCFtxBaAgAxDVCksOqRJclyuZ1VqhHJPbYe_Wt787DNGs25133UkjYKo48KliHcUHqvRtCB5rs_XdY_5gODO9PdPbM709c7TXzdwMMw0i_vOZBsa0ln9hGWsd</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Iye, Tetsuya</creator><creator>Wyk, Pieter van</creator><creator>Matsumoto, Takahiro</creator><creator>Susukida, Yuki</creator><creator>Takaya, Shohei</creator><creator>Fujii, Yoshimi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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 before calibration and no additional measurements only for calibration. 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 re-inputting patterns into the neural network, and discuss its effectiveness. 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| subjects | Anechoic chambers Antenna arrays Antenna measurements Antenna radiation patterns Artificial neural networks Calibration deep learning Failure rates Linear arrays neural network Neural networks Phased arrays Power measurement radiation patterns Training |
| title | Neural Network-Based Phase Estimation for Antenna Array Using Radiation Power Pattern |
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