Robust regularized least-squares beamforming approach to signal estimation

Mohamed Abdalla Elhag Suliman, Tarig Ballal, Tareq Y. Al-Naffouri

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

4 Scopus citations


In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors. The two vectors are linearly related to the steering vector and the received signal snapshot, respectively. The linear operator, in both cases, is the square root of the covariance matrix. A regularized least-squares (RLS) approach is proposed to estimate these two vectors and to provide robustness without exploiting prior information. Simulation results show that the RLS beamformer using the proposed regularization algorithm outperforms state-of-the-art beamforming algorithms, as well as another RLS beamformers using a standard regularization approaches.
Original languageEnglish (US)
Title of host publication2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Print)9781509045457
StatePublished - May 12 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): CRG_R2_13_ALOU_KAUST_2
Acknowledgements: This work was funded in part by a CRG2 grant CRG_R2_13_ALOU_KAUST_2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST).


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