| Full text | |
| Author(s): |
Mohammadzadeh, Saeed
;
Nascimento, Vitor H.
;
de Lamare, Rodrigo C.
;
Kukrer, Osman
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP); v. N/A, p. 5-pg., 2022-01-01. |
| Abstract | |
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) | |
| FAPESP's process: | 19/19387-9 - Signal processing techniques for beamforming and coding schemes in IoT communication systems |
| Grantee: | Saeed Mohammadzadeh |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 18/12579-7 - ELIOT: enabling technologies for IoT |
| Grantee: | Vitor Heloiz Nascimento |
| Support Opportunities: | Research Projects - Thematic Grants |