A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing
Reconstructing the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix is a good method for calculating the adaptive beamforming coefficients. However, when the directions-of-arrival (DOAs) and the number of the inte...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8736741/ |
| _version_ | 1818407571940179968 |
|---|---|
| author | Yuguan Hou Hefu Gao Qinghong Huang Jinzi Qi Xingpeng Mao Cunfeng Gu |
| author_facet | Yuguan Hou Hefu Gao Qinghong Huang Jinzi Qi Xingpeng Mao Cunfeng Gu |
| author_sort | Yuguan Hou |
| collection | DOAJ |
| description | Reconstructing the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix is a good method for calculating the adaptive beamforming coefficients. However, when the directions-of-arrival (DOAs) and the number of the interferences are unknown and the steering vector has an error, the reconstructed interference-plus-noise covariance matrix might not be accurate, which degrades the performance of adaptive beamforming. Here, we propose a robust Capon beamforming approach, which is suited to the sparse array with the array steering error and the unknown interference DOAs. In particular, by drawing a modified optimization problem and the mean shift model of the interference covariance matrix, we propose the robust beamforming with the importance resampling based compressive covariance sensing, which is shown to outperform the classical beamforming method based on reconstructing the interference-plus-noise covariance matrix. The key to our approach is the new solution of the reconstructing method and the important functions. The excellent performance of the proposed approach for interference suppression is demonstrated via a number of numerical examples. |
| first_indexed | 2024-12-14T09:29:58Z |
| format | Article |
| id | doaj.art-9e59093ec96e49f5932c190c58d221a6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T09:29:58Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9e59093ec96e49f5932c190c58d221a62022-12-21T23:08:06ZengIEEEIEEE Access2169-35362019-01-017804788049010.1109/ACCESS.2019.29230658736741A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance SensingYuguan Hou0Hefu Gao1https://orcid.org/0000-0002-3518-9793Qinghong Huang2Jinzi Qi3Xingpeng Mao4Cunfeng Gu5School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaFaculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The NetherlandsSchool of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, ChinaShanghai Electro-Mechanical Engineering Institute, Shanghai, ChinaReconstructing the interference-plus-noise covariance matrix instead of searching for the optimal diagonal loading factor for the sample covariance matrix is a good method for calculating the adaptive beamforming coefficients. However, when the directions-of-arrival (DOAs) and the number of the interferences are unknown and the steering vector has an error, the reconstructed interference-plus-noise covariance matrix might not be accurate, which degrades the performance of adaptive beamforming. Here, we propose a robust Capon beamforming approach, which is suited to the sparse array with the array steering error and the unknown interference DOAs. In particular, by drawing a modified optimization problem and the mean shift model of the interference covariance matrix, we propose the robust beamforming with the importance resampling based compressive covariance sensing, which is shown to outperform the classical beamforming method based on reconstructing the interference-plus-noise covariance matrix. The key to our approach is the new solution of the reconstructing method and the important functions. The excellent performance of the proposed approach for interference suppression is demonstrated via a number of numerical examples.https://ieeexplore.ieee.org/document/8736741/Adaptive beamformingcompressive covariance sensingsparse antenna arrayimportance resampling |
| spellingShingle | Yuguan Hou Hefu Gao Qinghong Huang Jinzi Qi Xingpeng Mao Cunfeng Gu A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing IEEE Access Adaptive beamforming compressive covariance sensing sparse antenna array importance resampling |
| title | A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing |
| title_full | A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing |
| title_fullStr | A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing |
| title_full_unstemmed | A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing |
| title_short | A Robust Capon Beamforming Approach for Sparse Array Based on Importance Resampling Compressive Covariance Sensing |
| title_sort | robust capon beamforming approach for sparse array based on importance resampling compressive covariance sensing |
| topic | Adaptive beamforming compressive covariance sensing sparse antenna array importance resampling |
| url | https://ieeexplore.ieee.org/document/8736741/ |
| work_keys_str_mv | AT yuguanhou arobustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT hefugao arobustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT qinghonghuang arobustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT jinziqi arobustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT xingpengmao arobustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT cunfenggu arobustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT yuguanhou robustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT hefugao robustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT qinghonghuang robustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT jinziqi robustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT xingpengmao robustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing AT cunfenggu robustcaponbeamformingapproachforsparsearraybasedonimportanceresamplingcompressivecovariancesensing |