J Shanghai Jiaotong Univ Sci ›› 2020, Vol. 25 ›› Issue (6): 790-794.doi: 10.1007/s12204-020-2222-z

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Reconstruction of Antenna Radiation Pattern Based on Compressed Sensing

ZHANG Haoping (张昊平), JIANG Yilin (蒋伊琳), LI Xiaoyu (李小钰)   

  1. (College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)
  • 出版日期:2020-12-28 发布日期:2020-11-26
  • 通讯作者: ZHANG Haoping (张昊平) E-mail:saopei@hrbeu.edu.cn

Reconstruction of Antenna Radiation Pattern Based on Compressed Sensing

ZHANG Haoping (张昊平), JIANG Yilin (蒋伊琳), LI Xiaoyu (李小钰)   

  1. (College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China)
  • Online:2020-12-28 Published:2020-11-26
  • Contact: ZHANG Haoping (张昊平) E-mail:saopei@hrbeu.edu.cn

摘要: The measurement of the far-field radiation pattern is an important factor in describing the characteristics of the antenna. The measurement process is time consuming and expensive. Therefore, this paper proposes a novel method to reduce the number of samples required for radiation pattern measurement by adopting compressed sensing theory. This method reconstructs the radiation pattern from data measured by a few sensors, and the positions of these sensors are generated via the m-sequence. Simulation results demonstrate that the proposed algorithm can effectively reconstruct the complete radiation pattern by using the 50% samples.


关键词: far-field, radiation pattern, compressed sensing, m-sequence

Abstract: The measurement of the far-field radiation pattern is an important factor in describing the characteristics of the antenna. The measurement process is time consuming and expensive. Therefore, this paper proposes a novel method to reduce the number of samples required for radiation pattern measurement by adopting compressed sensing theory. This method reconstructs the radiation pattern from data measured by a few sensors, and the positions of these sensors are generated via the m-sequence. Simulation results demonstrate that the proposed algorithm can effectively reconstruct the complete radiation pattern by using the 50% samples.


Key words: far-field, radiation pattern, compressed sensing, m-sequence

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