Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning
Aiming at the problem of scattering centers resolving and angular positions estimation of spatially extended targets, a high-resolution and high-accuracy angle estimation method based on multi-task group sparse model and collocated MIMO radar is proposed, which is helpful to obtain the structure inf...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8753576/ |
| _version_ | 1818935641424003072 |
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| author | Zhongfei Ni Binke Huang Meng Cao |
| author_facet | Zhongfei Ni Binke Huang Meng Cao |
| author_sort | Zhongfei Ni |
| collection | DOAJ |
| description | Aiming at the problem of scattering centers resolving and angular positions estimation of spatially extended targets, a high-resolution and high-accuracy angle estimation method based on multi-task group sparse model and collocated MIMO radar is proposed, which is helpful to obtain the structure information of targets and improve the success rate of target recognition. Characterized by sparse and clustered distribution in space, angular positions estimation of multiple closely-spaced and correlated point scattering targets belonging to a spatially extended target can be modeled as a multi-task group sparse problem and can be solved by multi-task group sparse recovery. To overcome the sparse recovery performance degradation caused by the high correlation in group sparse solution matrix and to improve the accuracy and robustness of angle estimation, a complex spatiotemporal sparse Bayesian learning (CST-SBL) algorithm which exploits spatiotemporal correlation structures of the solution matrix is proposed to reconstruct angular positions. Compared with previous work, the proposed approach achieves high-resolution and high-accuracy estimation performance, especially in cases of low SNR and few snapshots. The theoretical analysis and simulation results validate the effectiveness of the proposed technique. |
| first_indexed | 2024-12-20T05:23:24Z |
| format | Article |
| id | doaj.art-f68b29f891664d24b04101e116be5c57 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T05:23:24Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f68b29f891664d24b04101e116be5c572022-12-21T19:51:57ZengIEEEIEEE Access2169-35362019-01-017944739448010.1109/ACCESS.2019.29264428753576Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian LearningZhongfei Ni0https://orcid.org/0000-0003-0554-3226Binke Huang1https://orcid.org/0000-0002-4552-5914Meng Cao2School of Electronic and information Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Electronic and information Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Electronic and information Engineering, Xi’an Jiaotong University, Xi’an, ChinaAiming at the problem of scattering centers resolving and angular positions estimation of spatially extended targets, a high-resolution and high-accuracy angle estimation method based on multi-task group sparse model and collocated MIMO radar is proposed, which is helpful to obtain the structure information of targets and improve the success rate of target recognition. Characterized by sparse and clustered distribution in space, angular positions estimation of multiple closely-spaced and correlated point scattering targets belonging to a spatially extended target can be modeled as a multi-task group sparse problem and can be solved by multi-task group sparse recovery. To overcome the sparse recovery performance degradation caused by the high correlation in group sparse solution matrix and to improve the accuracy and robustness of angle estimation, a complex spatiotemporal sparse Bayesian learning (CST-SBL) algorithm which exploits spatiotemporal correlation structures of the solution matrix is proposed to reconstruct angular positions. Compared with previous work, the proposed approach achieves high-resolution and high-accuracy estimation performance, especially in cases of low SNR and few snapshots. The theoretical analysis and simulation results validate the effectiveness of the proposed technique.https://ieeexplore.ieee.org/document/8753576/Angular positions estimationspatially extended targetmulti-task group sparse modelspatiotemporal correlation structurescomplex spatiotemporal sparse Bayesian learning (CST-SBL) |
| spellingShingle | Zhongfei Ni Binke Huang Meng Cao Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning IEEE Access Angular positions estimation spatially extended target multi-task group sparse model spatiotemporal correlation structures complex spatiotemporal sparse Bayesian learning (CST-SBL) |
| title | Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning |
| title_full | Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning |
| title_fullStr | Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning |
| title_full_unstemmed | Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning |
| title_short | Angular Positions Estimation of Spatially Extended Targets for MIMO Radar Using Complex Spatiotemporal Sparse Bayesian Learning |
| title_sort | angular positions estimation of spatially extended targets for mimo radar using complex spatiotemporal sparse bayesian learning |
| topic | Angular positions estimation spatially extended target multi-task group sparse model spatiotemporal correlation structures complex spatiotemporal sparse Bayesian learning (CST-SBL) |
| url | https://ieeexplore.ieee.org/document/8753576/ |
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