Robust regularized least-squares beamforming approach to signal estimation

Handle URI:
http://hdl.handle.net/10754/623752
Title:
Robust regularized least-squares beamforming approach to signal estimation
Authors:
Suliman, Mohamed Abdalla Elhag ( 0000-0002-3447-1770 ) ; Ballal, Tarig; Al-Naffouri, Tareq Y.
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Suliman 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.
Publisher:
IEEE
Journal:
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
KAUST Grant Number:
CRG_R2_13_ALOU_KAUST_2
Conference/Event name:
2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016
Issue Date:
12-May-2017
DOI:
10.1109/GlobalSIP.2016.7905806
ARXIV:
arXiv:1611.06527
Type:
Conference Paper
Sponsors:
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).
Additional Links:
http://ieeexplore.ieee.org/document/7905806/; https://arxiv.org/abs/1611.06527
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSuliman, Mohamed Abdalla Elhagen
dc.contributor.authorBallal, Tarigen
dc.contributor.authorAl-Naffouri, Tareq Y.en
dc.date.accessioned2017-05-31T08:28:12Z-
dc.date.available2017-05-31T08:28:12Z-
dc.date.issued2017-05-12en
dc.identifier.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.en
dc.identifier.doi10.1109/GlobalSIP.2016.7905806en
dc.identifier.urihttp://hdl.handle.net/10754/623752-
dc.description.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.en
dc.description.sponsorshipThis 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).en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7905806/en
dc.relation.urlhttps://arxiv.org/abs/1611.06527en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectArray signal processingen
dc.subjectCovariance matricesen
dc.subjectEigenvalues and eigenfunctionsen
dc.subjectLinear systemsen
dc.subjectRobustnessen
dc.subjectSTEMen
dc.subjectUncertaintyen
dc.titleRobust regularized least-squares beamforming approach to signal estimationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)en
dc.conference.date2016-12-07 to 2016-12-09en
dc.conference.name2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016en
dc.conference.locationWashington, DC, USAen
dc.eprint.versionPost-printen
dc.identifier.arxividarXiv:1611.06527en
kaust.authorSuliman, Mohamed Abdalla Elhagen
kaust.authorBallal, Tarigen
kaust.authorAl-Naffouri, Tareq Y.en
kaust.grant.numberCRG_R2_13_ALOU_KAUST_2en
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