Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors
Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference. The presence of the desired signal components in the estimated noise and interference vectors is of important factors of error which signific...
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University of Qom
2023-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_2024_e75a89dd535ec72c9bb0b0dd5a1cf82f.pdf |
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| _version_ | 1832577526279438336 |
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| author | Saman Rezaeizadeh Mehdi Bekrani |
| author_facet | Saman Rezaeizadeh Mehdi Bekrani |
| author_sort | Saman Rezaeizadeh |
| collection | DOAJ |
| description | Minimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference. The presence of the desired signal components in the estimated noise and interference vectors is of important factors of error which significantly reduces the output SINR level of the beamformer. In this paper, in order to make the beamformer robust to the incorrect estimation of the data covariance matrix, a covariance matrix reconstruction method using the orthogonal steer vectors obtained by the Gram Schmidt algorithm along with a diagonal loading is employed. Simulation results show the superiority of the proposed method in the improvement of beam pattern, angle estimation of interferences, and output SINR level, compared to the counterparts. |
| format | Article |
| id | doaj-art-4678ca1a85824d748219b3867f7c369f |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2023-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-4678ca1a85824d748219b3867f7c369f2025-01-30T20:18:53ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752023-09-0191901072024Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal VectorsSaman Rezaeizadeh0Mehdi Bekrani1MSc. in Telecommunications, Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran. Email: rezaeizadeh.s@qut.ac.irAssistant Prof., Faculty of Electrical and Computer Engineering, Qom University of Technology, Qom, Iran. Email: bekrani@qut.ac.irMinimum Variance Beamforming methods, have a weak performance in situation where error is available in covariance matrix estimation of noise and interference. The presence of the desired signal components in the estimated noise and interference vectors is of important factors of error which significantly reduces the output SINR level of the beamformer. In this paper, in order to make the beamformer robust to the incorrect estimation of the data covariance matrix, a covariance matrix reconstruction method using the orthogonal steer vectors obtained by the Gram Schmidt algorithm along with a diagonal loading is employed. Simulation results show the superiority of the proposed method in the improvement of beam pattern, angle estimation of interferences, and output SINR level, compared to the counterparts.https://jemsc.qom.ac.ir/article_2024_e75a89dd535ec72c9bb0b0dd5a1cf82f.pdfbeamformingcovariance matrix reconstructioninterferenceminimum variance |
| spellingShingle | Saman Rezaeizadeh Mehdi Bekrani Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors مدیریت مهندسی و رایانش نرم beamforming covariance matrix reconstruction interference minimum variance |
| title | Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors |
| title_full | Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors |
| title_fullStr | Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors |
| title_full_unstemmed | Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors |
| title_short | Minimum Variance Beamforming Based on Covariance Matrix Reconstruction Using Orthogonal Vectors |
| title_sort | minimum variance beamforming based on covariance matrix reconstruction using orthogonal vectors |
| topic | beamforming covariance matrix reconstruction interference minimum variance |
| url | https://jemsc.qom.ac.ir/article_2024_e75a89dd535ec72c9bb0b0dd5a1cf82f.pdf |
| work_keys_str_mv | AT samanrezaeizadeh minimumvariancebeamformingbasedoncovariancematrixreconstructionusingorthogonalvectors AT mehdibekrani minimumvariancebeamformingbasedoncovariancematrixreconstructionusingorthogonalvectors |