Single frequency microwave imaging based on compressed sensing

Tianyi Zhou, Anjie Zhu, Yuzhou Shen, Huan Li, Changzhi Li, Jiangtao Hangfu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Microwave imaging has become a hot topic in the area of microwave engineering. In this paper, we propose a new approach for microwave imaging based on multiple transmit and receive antennas assisted with compressed sensing at single frequency. Different from compression achieved by frequency diversity relay on a wide bandwidth, compressed sensing is applied to figure out the location and shape of unknown target utilizing the spatially diversity of antennas' location. Thanks to the compressed sensing, inversion results can be obtained with much faster data processing than traditional methods. Numerical simulation of single target as well as multiple targets is investigated, and the results demonstrate the effectiveness of the proposed approach. With the simple implementation for fast imaging, our method can be potentially used in practical applications of microwave imaging.

Original languageEnglish
Title of host publicationRWS 2018 - Proceedings
Subtitle of host publication2018 IEEE Radio and Wireless Symposium
PublisherIEEE Computer Society
Pages133-135
Number of pages3
ISBN (Electronic)9781538607091
DOIs
StatePublished - Feb 28 2018
Event2018 IEEE Radio and Wireless Symposium, RWS 2018 - Anaheim, United States
Duration: Jan 14 2018Jan 17 2018

Publication series

NameIEEE Radio and Wireless Symposium, RWS
Volume2018-January
ISSN (Print)2164-2958
ISSN (Electronic)2164-2974

Conference

Conference2018 IEEE Radio and Wireless Symposium, RWS 2018
CountryUnited States
CityAnaheim
Period01/14/1801/17/18

Keywords

  • Born approximation
  • Compressed sensing
  • Microwave imaging

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    Zhou, T., Zhu, A., Shen, Y., Li, H., Li, C., & Hangfu, J. (2018). Single frequency microwave imaging based on compressed sensing. In RWS 2018 - Proceedings: 2018 IEEE Radio and Wireless Symposium (pp. 133-135). (IEEE Radio and Wireless Symposium, RWS; Vol. 2018-January). IEEE Computer Society. https://doi.org/10.1109/RWS.2018.8304966