Reconstruction of Antenna Radiation Pattern Based on Compressed Sensing

doi:10.1007/s12204-020-2222-z

Abstract

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

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

CLC Number:

TN 820

ZHANG Haoping, JIANG Yilin, LI Xiaoyu. Reconstruction of Antenna Radiation Pattern Based on Compressed Sensing[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 790-794.

References 12

[1]	DONOHO D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[2]	CANDES E J, WAKIN M B. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine,2008, 25(2): 21-30.
[3]	BARANIUK R, STEEGHS P. Compressive radar imaging [C]//IEEE Radar Conference. Boston, MA,USA: IEEE, 2007: 128-133.
[4]	GURBUZ A C, CEVHER V, MCCLELLAN J H. Bearing estimation via spatial sparsity using compressive sensing [J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(2): 1358-1369.
[5]	ROMBERG J. Imaging via compressive sampling [J].IEEE Signal Processing Magazine, 2008, 25(2): 14-20.
[6]	VERDIN B, DEBROUX P. Reconstruction of missing sections of radiation patterns using compressive sensing[C]//IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. Vancouver, BC, Canada: IEEE, 2015:780-781.
[7]	VERDIN B, DEBROUX P. 2D and 3D far-field radiation patterns reconstruction based on compressive sensing [J]. Progress in Electromagnetics Research M,2016, 46: 47-56.
[8]	CARIN L, LIU D, XUE Y. In situ compressive sensing [C]//2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.St. Thomas, VI, USA: IEEE, 2008: 105-108.
[9]	BARANIUK R G. Compressive sensing [J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-121.
[10]	LU W, LI W, KPALMA K, et al. Compressed sensing performance of random bernoulli matrices with high compression ratio [J]. IEEE Signal Processing Letters,2015, 22(8): 1074-1078.
[11]	TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory,2007, 53(12): 4655-4666.
[12]	LIU Y H. Design of m-sequence generator based on shift register with MATLAB [J]. Science & Technology View, 2011(25): 43-44 (in Chinese).