DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios
In this paper, a weighted l<sub>1</sub>-norm is proposed in a l<sub>1</sub>-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4614 |
| _version_ | 1797527879872937984 |
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| author | Ming Zuo Shuguo Xie Xian Zhang Meiling Yang |
| author_facet | Ming Zuo Shuguo Xie Xian Zhang Meiling Yang |
| author_sort | Ming Zuo |
| collection | DOAJ |
| description | In this paper, a weighted l<sub>1</sub>-norm is proposed in a l<sub>1</sub>-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l<sub>1</sub>-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l<sub>1</sub>-norm approximate the original l<sub>0</sub>-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station. |
| first_indexed | 2024-03-10T09:50:05Z |
| format | Article |
| id | doaj.art-a42a0e3c68f2407486cab05ce1a18828 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:50:05Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a42a0e3c68f2407486cab05ce1a188282023-11-22T02:52:06ZengMDPI AGSensors1424-82202021-07-012113461410.3390/s21134614DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR ScenariosMing Zuo0Shuguo Xie1Xian Zhang2Meiling Yang3School of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaIn this paper, a weighted l<sub>1</sub>-norm is proposed in a l<sub>1</sub>-norm-based singular value decomposition (L1-SVD) algorithm, which can suppress spurious peaks and improve accuracy of direction of arrival (DOA) estimation for the low signal-to-noise (SNR) scenarios. The weighted matrix is determined by optimizing the orthogonality of subspace, and the weighted l<sub>1</sub>-norm is used as the minimum objective function to increase the signal sparsity. Thereby, the weighted matrix makes the l<sub>1</sub>-norm approximate the original l<sub>0</sub>-norm. Simulated results of orthogonal frequency division multiplexing (OFDM) signal demonstrate that the proposed algorithm has s narrower main lobe and lower side lobe with the characteristics of fewer snapshots and low sensitivity of misestimated signals, which can improve the resolution and accuracy of DOA estimation. Specifically, the proposed method exhibits a better performance than other works for the low SNR scenarios. Outdoor experimental results of OFDM signals show that the proposed algorithm is superior to other methods with a narrower main lobe and lower side lobe, which can be used for DOA estimation of UAV and pseudo base station.https://www.mdpi.com/1424-8220/21/13/4614direction of arrival (DOA) estimationsparse representationlow signal to noiseweighted l<sub>1</sub>-norm |
| spellingShingle | Ming Zuo Shuguo Xie Xian Zhang Meiling Yang DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios Sensors direction of arrival (DOA) estimation sparse representation low signal to noise weighted l<sub>1</sub>-norm |
| title | DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios |
| title_full | DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios |
| title_fullStr | DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios |
| title_full_unstemmed | DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios |
| title_short | DOA Estimation Based on Weighted l<sub>1</sub>-norm Sparse Representation for Low SNR Scenarios |
| title_sort | doa estimation based on weighted l sub 1 sub norm sparse representation for low snr scenarios |
| topic | direction of arrival (DOA) estimation sparse representation low signal to noise weighted l<sub>1</sub>-norm |
| url | https://www.mdpi.com/1424-8220/21/13/4614 |
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