DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty
This paper proposes a new algorithm based on sparse signal recovery for estimating the direction of arrival (DOA) of multiple sources. The problem model we build is about the sample covariance matrix fitting by unknown source powers. We enhance the sparsity by the double-threshold sigmoid penalty fu...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/287915 |
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| _version_ | 1832558889115058176 |
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| author | Hanbing Wang Hui Li Bin Li |
| author_facet | Hanbing Wang Hui Li Bin Li |
| author_sort | Hanbing Wang |
| collection | DOAJ |
| description | This paper proposes a new algorithm based on sparse signal recovery for estimating the direction of arrival (DOA) of multiple sources. The problem model we build is about the sample covariance matrix fitting by unknown source powers. We enhance the sparsity by the double-threshold sigmoid penalty function which can approximate the l0 norm accurately. Our method can distinguish closely spaced sources and does not need the knowledge of the number of the sources. In addition, our method can also perform well in low SNR. Besides, our method can handle more sources accurately than other methods. Simulations are done to certify the great performance of the proposed method. |
| format | Article |
| id | doaj-art-ba30169acf674952b1a500b7db25920d |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-ba30169acf674952b1a500b7db25920d2025-02-03T01:31:19ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/287915287915DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid PenaltyHanbing Wang0Hui Li1Bin Li2Department of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, ChinaDepartment of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, ChinaDepartment of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710129, ChinaThis paper proposes a new algorithm based on sparse signal recovery for estimating the direction of arrival (DOA) of multiple sources. The problem model we build is about the sample covariance matrix fitting by unknown source powers. We enhance the sparsity by the double-threshold sigmoid penalty function which can approximate the l0 norm accurately. Our method can distinguish closely spaced sources and does not need the knowledge of the number of the sources. In addition, our method can also perform well in low SNR. Besides, our method can handle more sources accurately than other methods. Simulations are done to certify the great performance of the proposed method.http://dx.doi.org/10.1155/2015/287915 |
| spellingShingle | Hanbing Wang Hui Li Bin Li DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty Journal of Electrical and Computer Engineering |
| title | DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty |
| title_full | DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty |
| title_fullStr | DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty |
| title_full_unstemmed | DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty |
| title_short | DOA Estimation Based on Sparse Signal Recovery Utilizing Double-Threshold Sigmoid Penalty |
| title_sort | doa estimation based on sparse signal recovery utilizing double threshold sigmoid penalty |
| url | http://dx.doi.org/10.1155/2015/287915 |
| work_keys_str_mv | AT hanbingwang doaestimationbasedonsparsesignalrecoveryutilizingdoublethresholdsigmoidpenalty AT huili doaestimationbasedonsparsesignalrecoveryutilizingdoublethresholdsigmoidpenalty AT binli doaestimationbasedonsparsesignalrecoveryutilizingdoublethresholdsigmoidpenalty |