A Compressive Sensing Approach for Single-Snapshot Adaptive Beamforming
ID:109 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:351 Oral Presentation

Start Time:2020-06-08 14:00(Asia/Shanghai)

Duration:20min

Session:S Special Session » SS11Recent Advances In Beamforming Techniques And Applications

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Abstract
This paper introduces a compressive sensing approach for single-snapshot adaptive beamforming. The observation data model is considered as source components in additive noise, and then a compressive sensing formulation is introduced to estimate the parameters of the interference signals. That is, a LASSO regression problem is proposed and solved, yielding the directions as well as the powers of the interference signals. On the other hand, the noise power is estimated by means of averaging the squares of the difference between the observation data and the estimate of the source components. Finally, the interference-plus-noise covariance matrix is reconstructed and used for adaptive beamformer design. Simulation results show better performance of the proposed beamformer than several existing beamformers, in the case of a single snapshot.
Keywords
Adaptive beamforming; single snapshot; compressive sensing; covariance matrix reconstruction; sparsity
Speaker
Huiping Huang
Darmstadt University of Technology, Germany

Submission Author
Huiping Huang Darmstadt University of Technology, Germany
Abdelhak M Darmstadt University of Technology, Germany
Hing Cheung City University of Hong Kong, Hong Kong
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Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

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  • Dec 31 2020

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