BCS Based MIMO Sparse Array Synthesis for Near-Field Imaging

Yixin Huang*, Shuoguang Wang, Shiyong Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a Multiple-Input Multiple -Output (MIMO) sparse array synthesis (SAS) method for wideband near-field imaging based on multi-task Bayesian compressive sensing (MT BCS). By applying the classic SAS model, MT BCS is used to statistically combine the real and imaginary parts of complex excitations with the shared prior to construct the complex excitations. Specifically, MT BCS adds elements with non-zero weights successfully to the model until all elements are included. This greatly reduces computation complexity. Hence, the proposed method exhibits obviously improved synthesizing speed over the classic CS-based MIMO SAS method. Finally, the efficacy of the proposed method is verified through the experimental results.

Original languageEnglish
Title of host publication2023 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350338874
DOIs
Publication statusPublished - 2023
Event15th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023 - Qingdao, China
Duration: 14 May 202317 May 2023

Publication series

Name2023 International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023 - Proceedings

Conference

Conference15th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023
Country/TerritoryChina
CityQingdao
Period14/05/2317/05/23

Keywords

  • Multiple-Input Multiple-Output(MIMO)
  • Near-field imaging
  • multi-task Bayesian compressive sensing (MT BCS)
  • sparse array synthesizing (SAS)

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