Single-snapshot adaptive beamforming

Yujie Gu, Yimin D. Zhang

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

8 Scopus citations


Adaptive beamformers are sensitive to model mismatch, especially when the number of training samples is small or the training samples are contaminated by the signal component. In this paper, we consider an extreme scenario where only a single signal-contaminated snapshot is available for adaptive beamformer design. In such a case, we cannot perform direct inversion or eigen-decomposition of the rank-one sample covariance matrix required in adaptive beamformer design. To address this issue, we formulate a sparsity-constrained covariance matrix fitting problem to estimate the spatial spectrum distribution over the observed spatial domain, which is then used for adaptive beamformer design via the sparse reconstruction of the interference-plus-noise covariance matrix. Simulation results demonstrate the performance advantage of the proposed adaptive beamforming algorithm over other beamforming algorithms suitable for the single-snapshot scenario.

Original languageEnglish
Title of host publication2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PublisherIEEE Computer Society
Number of pages5
ISBN (Print)9781538647523
StatePublished - 27 Aug 2018
Externally publishedYes
Event10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 - Sheffield, United Kingdom
Duration: 8 Jul 201811 Jul 2018

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)2151-870X


Conference10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2018 IEEE.


  • Adaptive beamforming
  • Covariance matrix fitting
  • Covariance matrix reconstruction
  • Single snapshot
  • Sparsity


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