| Texto completo | |
| Autor(es): |
Mohammadzadeh, Saeed
;
Nascimento, Vitor H.
;
de Lamare, Rodrigo C.
;
Kukrer, Osman
;
IEEE
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2022-01-01. |
| Resumo | |
Robust adaptive beamforming (RAB) based on interference-plusnoise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix reconstruction issues. The proposed method involves INC matrix reconstruction using an idea in which the power and the steering vector of the interferences are estimated based on the power method. Furthermore, spatial match processing is computed to reconstruct the desired signal-plus-noise covariance matrix. Then, the noise components are excluded to retain the desired signal (DS) covariance matrix. A key feature of the proposed technique is to avoid eigenvalue decomposition of the INC matrix to obtain the dominant power of the interference-plus-noise region. Moreover, the INC reconstruction is carried out according to the definition of the theoretical INC matrix. Simulation results are shown and discussed to verify the effectiveness of the proposed method against existing approaches. (AU) | |
| Processo FAPESP: | 19/19387-9 - Técnicas de processamento de sinais em beamforming e esquemas de codificação em sistemas de comunicações de IoT |
| Beneficiário: | Saeed Mohammadzadeh |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 18/12579-7 - Tecnologias habilitadores para a Internet das Coisas |
| Beneficiário: | Vitor Heloiz Nascimento |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |