Robust regularized least-squares beamforming approach to signal estimation
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberCRG_R2_13_ALOU_KAUST_2
Preprint Posting Date2016-11-20
Online Publication Date2017-05-12
Print Publication Date2016-12
Permanent link to this recordhttp://hdl.handle.net/10754/623752
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AbstractIn 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.
CitationSuliman M, Ballal T, Al-Naffouri TY (2016) Robust regularized least-squares beamforming approach to signal estimation. 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Available: http://dx.doi.org/10.1109/GlobalSIP.2016.7905806.
SponsorsThis 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).
Conference/Event name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016