Adaptive beamforming with sensor position errors using covariance matrix construction based on subspace bases transition

Peng Chen, Yixin Yang, Yong Wang, Yuanliang Ma

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

This letter proposes a narrowband interference-plus-noise covariance matrix (INCM) based beamformer, which is robust with sensor position errors for linear array. First, using the subspace fitting and subspace orthogonality techniques, we estimate a set of angle-related bases for the signal-plus-interference subspace (SIS) by solving a joint optimization problem. Second, we obtain the bases transition matrix between the estimated angle-related bases and the orthogonal bases consisting of the dominant eigenvectors of the sample covariance matrix (SCM). The SCM can be expressed as a function of the angle-related bases and the bases transition matrix. We construct the INCM directly from the SIS by eliminating the component of the desired signal from the angle-related bases. Simulations and experimental results show that the proposed beamformer outperforms other tested beamformers in the presence of sensor position errors.

Original languageEnglish
Article number8516386
Pages (from-to)19-23
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number1
DOIs
StatePublished - Jan 2019

Keywords

  • Bases transition
  • covariance matrix construction
  • robust adaptive beamforming
  • sensor position error

Fingerprint

Dive into the research topics of 'Adaptive beamforming with sensor position errors using covariance matrix construction based on subspace bases transition'. Together they form a unique fingerprint.

Cite this