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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Robust adaptive b eamforming base d on virtual sensors using low-complexity spatial sampling

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Author(s):
Mohammadzadeh, Saeed [1] ; Nascimento, Vitor H. [1] ; de Lamare, Rodrigo C. [2] ; Kukrer, Osman [3]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Dept Elect Syst Engn, Sao Paulo - Brazil
[2] Pontificia Univ Catolica Rio de Janeiro, CETUC, Rio de Janeiro - Brazil
[3] Eastern Mediterranean Univ, Dept Elect & Elect Engn, Famagusta - Turkey
Total Affiliations: 3
Document type: Journal article
Source: Signal Processing; v. 188, NOV 2021.
Web of Science Citations: 0
Abstract

The performance of robust adaptive beamforming (RAB) based on interference-plus-noise covariance (IPNC) matrix reconstruction can be degraded seriously in the presence of random mismatches (look direction and array geometry), particularly when the input signal-to-noise ratio (SNR) is high. In this work, we present a RAB technique to address covariance matrix reconstruction problems. The proposed RAB technique involves IPNC matrix reconstruction using a low-complexity spatial sampling process (LCSSP) and employs a virtual received array vector. In particular, the power spectrum sampling is realized by a proposed projection matrix in a higher dimension. The essence of the proposed technique is to avoid reconstruction of the IPNC matrix by integrating over the angular sector of the interference-plus-noise region. Simulation results are presented to verify the effectiveness of the proposed RAB approach. (c) 2021 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 18/12579-7 - ELIOT: enabling technologies for IoT
Grantee:Vitor Heloiz Nascimento
Support Opportunities: Research Projects - Thematic Grants