Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming
Aiming to address the problem of increased radar jamming in complex electromagnetic environments and the difficulty of accurately estimating the target signal close to a strong jamming signal, this paper proposes a sparse Direction of Arrival (DOA) estimation method based on Riemann averaging under...
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2025-04-01
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| Series: | Leida xuebao |
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| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24175 |
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| author | Xiaoyu LAN Jiyan HU Mingshen LIANG Shuang MA |
| author_facet | Xiaoyu LAN Jiyan HU Mingshen LIANG Shuang MA |
| author_sort | Xiaoyu LAN |
| collection | DOAJ |
| description | Aiming to address the problem of increased radar jamming in complex electromagnetic environments and the difficulty of accurately estimating the target signal close to a strong jamming signal, this paper proposes a sparse Direction of Arrival (DOA) estimation method based on Riemann averaging under strong intermittent jamming. First, under the extended coprime array data model, the Riemann averaging is introduced to suppress the jamming signal by leveraging the property that the target signal is continuously active while the strong jamming signal is intermittently active. Then, the covariance matrix of the processed data is vectorized to obtain virtual array reception data. Finally, the sparse iterative covariance-based estimation method, which is used for estimating the DOA under strong intermittent interference, is employed in the virtual domain to reconstruct the sparse signal and estimate the DOA of the target signal. The simulation results show that the method can provide highly accurate DOA estimation for weak target signals whose angles are closely adjacent to strong interference signals when the number of signal sources is unknown. Compared with existing subspace algorithms and sparse reconstruction class algorithms, the proposed algorithm has higher estimation accuracy and angular resolution at a smaller number of snapshots, as well as a lower signal-to-noise ratio. |
| format | Article |
| id | doaj-art-0a91ed2b08b841a0adfda2494206e8cb |
| institution | DOAJ |
| issn | 2095-283X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj-art-0a91ed2b08b841a0adfda2494206e8cb2025-08-20T02:53:33ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2025-04-0114228029210.12000/JR24175R24175Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent JammingXiaoyu LAN0Jiyan HU1Mingshen LIANG2Shuang MA3College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaCollege of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaCollege of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaCollege of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaAiming to address the problem of increased radar jamming in complex electromagnetic environments and the difficulty of accurately estimating the target signal close to a strong jamming signal, this paper proposes a sparse Direction of Arrival (DOA) estimation method based on Riemann averaging under strong intermittent jamming. First, under the extended coprime array data model, the Riemann averaging is introduced to suppress the jamming signal by leveraging the property that the target signal is continuously active while the strong jamming signal is intermittently active. Then, the covariance matrix of the processed data is vectorized to obtain virtual array reception data. Finally, the sparse iterative covariance-based estimation method, which is used for estimating the DOA under strong intermittent interference, is employed in the virtual domain to reconstruct the sparse signal and estimate the DOA of the target signal. The simulation results show that the method can provide highly accurate DOA estimation for weak target signals whose angles are closely adjacent to strong interference signals when the number of signal sources is unknown. Compared with existing subspace algorithms and sparse reconstruction class algorithms, the proposed algorithm has higher estimation accuracy and angular resolution at a smaller number of snapshots, as well as a lower signal-to-noise ratio.https://radars.ac.cn/cn/article/doi/10.12000/JR24175direction of arrival (doa) estimationcoprime arrayriemann averagingjamming suppressionsparse iterative covariance-based estimation (spice) |
| spellingShingle | Xiaoyu LAN Jiyan HU Mingshen LIANG Shuang MA Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming Leida xuebao direction of arrival (doa) estimation coprime array riemann averaging jamming suppression sparse iterative covariance-based estimation (spice) |
| title | Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming |
| title_full | Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming |
| title_fullStr | Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming |
| title_full_unstemmed | Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming |
| title_short | Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming |
| title_sort | sparse doa estimation method based on riemann averaging under strong intermittent jamming |
| topic | direction of arrival (doa) estimation coprime array riemann averaging jamming suppression sparse iterative covariance-based estimation (spice) |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR24175 |
| work_keys_str_mv | AT xiaoyulan sparsedoaestimationmethodbasedonriemannaveragingunderstrongintermittentjamming AT jiyanhu sparsedoaestimationmethodbasedonriemannaveragingunderstrongintermittentjamming AT mingshenliang sparsedoaestimationmethodbasedonriemannaveragingunderstrongintermittentjamming AT shuangma sparsedoaestimationmethodbasedonriemannaveragingunderstrongintermittentjamming |