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

Autor(es)

Mohammadzadeh, Saeed ^[1] ; Nascimento, Vitor H. ^[1] ; de Lamare, Rodrigo C. ^[2] ; Kukrer, Osman ^[3]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: Signal Processing; v. 188, NOV 2021.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 18/12579-7 - Tecnologias habilitadores para a Internet das Coisas
• Beneficiário: Vitor Heloiz Nascimento
• Modalidade de apoio: Auxílio à Pesquisa - Temático